linear_regression_by_year_month_individual_depts.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import numpy as np\n",
    "import datetime as dt\n",
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Production"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_id</th>\n",
       "      <th>release_date</th>\n",
       "      <th>percent_female</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11670</th>\n",
       "      <td>43395</td>\n",
       "      <td>1950-01-01</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8717</th>\n",
       "      <td>83015</td>\n",
       "      <td>1950-01-01</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10830</th>\n",
       "      <td>109364</td>\n",
       "      <td>1950-01-01</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1576</th>\n",
       "      <td>43391</td>\n",
       "      <td>1950-01-04</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10697</th>\n",
       "      <td>151310</td>\n",
       "      <td>1950-01-13</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14964</th>\n",
       "      <td>354282</td>\n",
       "      <td>2017-08-31</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14437</th>\n",
       "      <td>374471</td>\n",
       "      <td>2017-09-14</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14133</th>\n",
       "      <td>300665</td>\n",
       "      <td>2017-09-14</td>\n",
       "      <td>0.111111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14984</th>\n",
       "      <td>398818</td>\n",
       "      <td>2017-10-27</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15452</th>\n",
       "      <td>341689</td>\n",
       "      <td>2017-12-27</td>\n",
       "      <td>0.285714</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15675 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       movie_id release_date  percent_female\n",
       "11670     43395   1950-01-01        0.000000\n",
       "8717      83015   1950-01-01        0.000000\n",
       "10830    109364   1950-01-01        0.000000\n",
       "1576      43391   1950-01-04        0.000000\n",
       "10697    151310   1950-01-13        0.000000\n",
       "...         ...          ...             ...\n",
       "14964    354282   2017-08-31        1.000000\n",
       "14437    374471   2017-09-14        0.500000\n",
       "14133    300665   2017-09-14        0.111111\n",
       "14984    398818   2017-10-27        0.200000\n",
       "15452    341689   2017-12-27        0.285714\n",
       "\n",
       "[15675 rows x 3 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load file\n",
    "production_df = pd.read_csv(\"Resources/percent_production.csv\")\n",
    "production_df = production_df.sort_values('release_date')\n",
    "production_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movie_id            int64\n",
       "release_date       object\n",
       "percent_female    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "production_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movie_id                   int64\n",
       "release_date      datetime64[ns]\n",
       "percent_female           float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# change release_date column from string to datetime\n",
    "# https://stackoverflow.com/questions/40217369/python-linear-regression-predict-by-date\n",
    "# https://pandas.pydata.org/docs/reference/api/pandas.Timestamp.toordinal.html\n",
    "production_df['release_date'] = pd.to_datetime(production_df['release_date'])\n",
    "\n",
    "production_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_id</th>\n",
       "      <th>release_date</th>\n",
       "      <th>percent_female</th>\n",
       "      <th>year_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11670</th>\n",
       "      <td>43395</td>\n",
       "      <td>1950-01-01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1950-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8717</th>\n",
       "      <td>83015</td>\n",
       "      <td>1950-01-01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1950-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10830</th>\n",
       "      <td>109364</td>\n",
       "      <td>1950-01-01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1950-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1576</th>\n",
       "      <td>43391</td>\n",
       "      <td>1950-01-04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1950-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10697</th>\n",
       "      <td>151310</td>\n",
       "      <td>1950-01-13</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1950-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14964</th>\n",
       "      <td>354282</td>\n",
       "      <td>2017-08-31</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2017-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14437</th>\n",
       "      <td>374471</td>\n",
       "      <td>2017-09-14</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>2017-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14133</th>\n",
       "      <td>300665</td>\n",
       "      <td>2017-09-14</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>2017-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14984</th>\n",
       "      <td>398818</td>\n",
       "      <td>2017-10-27</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>2017-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15452</th>\n",
       "      <td>341689</td>\n",
       "      <td>2017-12-27</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>2017-12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15675 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       movie_id release_date  percent_female year_month\n",
       "11670     43395   1950-01-01        0.000000    1950-01\n",
       "8717      83015   1950-01-01        0.000000    1950-01\n",
       "10830    109364   1950-01-01        0.000000    1950-01\n",
       "1576      43391   1950-01-04        0.000000    1950-01\n",
       "10697    151310   1950-01-13        0.000000    1950-01\n",
       "...         ...          ...             ...        ...\n",
       "14964    354282   2017-08-31        1.000000    2017-08\n",
       "14437    374471   2017-09-14        0.500000    2017-09\n",
       "14133    300665   2017-09-14        0.111111    2017-09\n",
       "14984    398818   2017-10-27        0.200000    2017-10\n",
       "15452    341689   2017-12-27        0.285714    2017-12\n",
       "\n",
       "[15675 rows x 4 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use Pandas.to_datetime() and datetime.strftime() method to add year_month column\n",
    "production_df['year_month'] = pd.to_datetime(production_df['release_date']).dt.strftime('%Y-%m')\n",
    "\n",
    "production_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movie_id                   int64\n",
       "release_date      datetime64[ns]\n",
       "percent_female           float64\n",
       "year_month                object\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "production_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movie_id                   int64\n",
       "release_date      datetime64[ns]\n",
       "percent_female           float64\n",
       "year_month        datetime64[ns]\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert year_month column to datetime format\n",
    "production_df['year_month'] = pd.to_datetime(production_df['year_month'])\n",
    "\n",
    "production_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movie_id                   int64\n",
       "release_date      datetime64[ns]\n",
       "percent_female           float64\n",
       "year_month                 int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert year_month to ordinal for ML\n",
    "production_df['year_month']=production_df['year_month'].map(dt.datetime.toordinal)\n",
    "\n",
    "production_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year_month\n",
       "711858    0.000000\n",
       "711889    0.000000\n",
       "711917    0.000000\n",
       "711948    0.000000\n",
       "711978    0.000000\n",
       "            ...   \n",
       "736511    0.228992\n",
       "736542    0.429702\n",
       "736573    0.305556\n",
       "736603    0.200000\n",
       "736664    0.285714\n",
       "Name: percent_female, Length: 815, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prod_count = production_df.groupby(['year_month'])['percent_female'].mean()\n",
    "\n",
    "prod_count"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DONT RERUN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year_month</th>\n",
       "      <th>percent_female</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>711858</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>711889</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>711917</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>711948</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>711978</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>810</th>\n",
       "      <td>736511</td>\n",
       "      <td>0.228992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>811</th>\n",
       "      <td>736542</td>\n",
       "      <td>0.429702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>812</th>\n",
       "      <td>736573</td>\n",
       "      <td>0.305556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>813</th>\n",
       "      <td>736603</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>814</th>\n",
       "      <td>736664</td>\n",
       "      <td>0.285714</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>815 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     year_month  percent_female\n",
       "0        711858        0.000000\n",
       "1        711889        0.000000\n",
       "2        711917        0.000000\n",
       "3        711948        0.000000\n",
       "4        711978        0.000000\n",
       "..          ...             ...\n",
       "810      736511        0.228992\n",
       "811      736542        0.429702\n",
       "812      736573        0.305556\n",
       "813      736603        0.200000\n",
       "814      736664        0.285714\n",
       "\n",
       "[815 rows x 2 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Change from series to df\n",
    "production = prod_count.to_frame()\n",
    "\n",
    "# Reset index  DO NOT RERUN\n",
    "# Reset index  DO NOT RERUN\n",
    "production = production.rename_axis('year_month').reset_index()\n",
    "\n",
    "# Review df\n",
    "production"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Review data distribution before applying ML model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Preview data\n",
    "plt.scatter(production.year_month, production.percent_female)\n",
    "plt.xlabel('Release Date')\n",
    "plt.ylabel('Percentage of Female Production Staff')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[711858],\n",
       "       [711889],\n",
       "       [711917],\n",
       "       [711948],\n",
       "       [711978],\n",
       "       [712009],\n",
       "       [712039],\n",
       "       [712070],\n",
       "       [712101],\n",
       "       [712131],\n",
       "       [712162],\n",
       "       [712192],\n",
       "       [712223],\n",
       "       [712254],\n",
       "       [712282],\n",
       "       [712313],\n",
       "       [712343],\n",
       "       [712374],\n",
       "       [712404],\n",
       "       [712435],\n",
       "       [712466],\n",
       "       [712496],\n",
       "       [712527],\n",
       "       [712557],\n",
       "       [712588],\n",
       "       [712619],\n",
       "       [712648],\n",
       "       [712679],\n",
       "       [712709],\n",
       "       [712740],\n",
       "       [712770],\n",
       "       [712801],\n",
       "       [712832],\n",
       "       [712862],\n",
       "       [712893],\n",
       "       [712923],\n",
       "       [712954],\n",
       "       [712985],\n",
       "       [713013],\n",
       "       [713044],\n",
       "       [713074],\n",
       "       [713105],\n",
       "       [713135],\n",
       "       [713166],\n",
       "       [713197],\n",
       "       [713227],\n",
       "       [713258],\n",
       "       [713288],\n",
       "       [713319],\n",
       "       [713350],\n",
       "       [713378],\n",
       "       [713409],\n",
       "       [713439],\n",
       "       [713470],\n",
       "       [713500],\n",
       "       [713531],\n",
       "       [713562],\n",
       "       [713592],\n",
       "       [713623],\n",
       "       [713653],\n",
       "       [713684],\n",
       "       [713715],\n",
       "       [713743],\n",
       "       [713774],\n",
       "       [713804],\n",
       "       [713835],\n",
       "       [713865],\n",
       "       [713896],\n",
       "       [713927],\n",
       "       [713957],\n",
       "       [713988],\n",
       "       [714018],\n",
       "       [714049],\n",
       "       [714080],\n",
       "       [714109],\n",
       "       [714140],\n",
       "       [714170],\n",
       "       [714201],\n",
       "       [714231],\n",
       "       [714262],\n",
       "       [714293],\n",
       "       [714323],\n",
       "       [714354],\n",
       "       [714384],\n",
       "       [714415],\n",
       "       [714446],\n",
       "       [714474],\n",
       "       [714505],\n",
       "       [714535],\n",
       "       [714566],\n",
       "       [714596],\n",
       "       [714627],\n",
       "       [714658],\n",
       "       [714688],\n",
       "       [714719],\n",
       "       [714749],\n",
       "       [714780],\n",
       "       [714811],\n",
       "       [714839],\n",
       "       [714870],\n",
       "       [714900],\n",
       "       [714931],\n",
       "       [714961],\n",
       "       [714992],\n",
       "       [715023],\n",
       "       [715053],\n",
       "       [715084],\n",
       "       [715114],\n",
       "       [715145],\n",
       "       [715176],\n",
       "       [715204],\n",
       "       [715235],\n",
       "       [715265],\n",
       "       [715296],\n",
       "       [715326],\n",
       "       [715357],\n",
       "       [715388],\n",
       "       [715418],\n",
       "       [715449],\n",
       "       [715479],\n",
       "       [715510],\n",
       "       [715541],\n",
       "       [715570],\n",
       "       [715601],\n",
       "       [715631],\n",
       "       [715662],\n",
       "       [715692],\n",
       "       [715723],\n",
       "       [715754],\n",
       "       [715784],\n",
       "       [715815],\n",
       "       [715845],\n",
       "       [715876],\n",
       "       [715907],\n",
       "       [715935],\n",
       "       [715966],\n",
       "       [715996],\n",
       "       [716027],\n",
       "       [716057],\n",
       "       [716088],\n",
       "       [716119],\n",
       "       [716149],\n",
       "       [716180],\n",
       "       [716210],\n",
       "       [716241],\n",
       "       [716272],\n",
       "       [716300],\n",
       "       [716331],\n",
       "       [716361],\n",
       "       [716392],\n",
       "       [716422],\n",
       "       [716453],\n",
       "       [716484],\n",
       "       [716514],\n",
       "       [716545],\n",
       "       [716575],\n",
       "       [716606],\n",
       "       [716637],\n",
       "       [716665],\n",
       "       [716696],\n",
       "       [716726],\n",
       "       [716757],\n",
       "       [716787],\n",
       "       [716818],\n",
       "       [716849],\n",
       "       [716879],\n",
       "       [716910],\n",
       "       [716940],\n",
       "       [716971],\n",
       "       [717002],\n",
       "       [717031],\n",
       "       [717062],\n",
       "       [717092],\n",
       "       [717123],\n",
       "       [717153],\n",
       "       [717184],\n",
       "       [717215],\n",
       "       [717245],\n",
       "       [717276],\n",
       "       [717306],\n",
       "       [717337],\n",
       "       [717368],\n",
       "       [717396],\n",
       "       [717427],\n",
       "       [717457],\n",
       "       [717488],\n",
       "       [717518],\n",
       "       [717549],\n",
       "       [717580],\n",
       "       [717610],\n",
       "       [717641],\n",
       "       [717671],\n",
       "       [717702],\n",
       "       [717733],\n",
       "       [717761],\n",
       "       [717792],\n",
       "       [717822],\n",
       "       [717853],\n",
       "       [717883],\n",
       "       [717914],\n",
       "       [717945],\n",
       "       [717975],\n",
       "       [718006],\n",
       "       [718036],\n",
       "       [718067],\n",
       "       [718098],\n",
       "       [718126],\n",
       "       [718157],\n",
       "       [718187],\n",
       "       [718218],\n",
       "       [718248],\n",
       "       [718279],\n",
       "       [718310],\n",
       "       [718340],\n",
       "       [718371],\n",
       "       [718401],\n",
       "       [718432],\n",
       "       [718463],\n",
       "       [718492],\n",
       "       [718523],\n",
       "       [718553],\n",
       "       [718584],\n",
       "       [718614],\n",
       "       [718645],\n",
       "       [718676],\n",
       "       [718706],\n",
       "       [718737],\n",
       "       [718767],\n",
       "       [718798],\n",
       "       [718829],\n",
       "       [718857],\n",
       "       [718888],\n",
       "       [718918],\n",
       "       [718949],\n",
       "       [718979],\n",
       "       [719010],\n",
       "       [719041],\n",
       "       [719071],\n",
       "       [719102],\n",
       "       [719132],\n",
       "       [719163],\n",
       "       [719194],\n",
       "       [719222],\n",
       "       [719253],\n",
       "       [719283],\n",
       "       [719314],\n",
       "       [719344],\n",
       "       [719375],\n",
       "       [719406],\n",
       "       [719436],\n",
       "       [719467],\n",
       "       [719497],\n",
       "       [719528],\n",
       "       [719559],\n",
       "       [719587],\n",
       "       [719618],\n",
       "       [719648],\n",
       "       [719679],\n",
       "       [719709],\n",
       "       [719740],\n",
       "       [719771],\n",
       "       [719801],\n",
       "       [719832],\n",
       "       [719862],\n",
       "       [719893],\n",
       "       [719924],\n",
       "       [719953],\n",
       "       [719984],\n",
       "       [720014],\n",
       "       [720045],\n",
       "       [720075],\n",
       "       [720106],\n",
       "       [720137],\n",
       "       [720167],\n",
       "       [720198],\n",
       "       [720228],\n",
       "       [720259],\n",
       "       [720290],\n",
       "       [720318],\n",
       "       [720349],\n",
       "       [720379],\n",
       "       [720410],\n",
       "       [720440],\n",
       "       [720471],\n",
       "       [720502],\n",
       "       [720532],\n",
       "       [720563],\n",
       "       [720593],\n",
       "       [720624],\n",
       "       [720655],\n",
       "       [720683],\n",
       "       [720714],\n",
       "       [720744],\n",
       "       [720775],\n",
       "       [720805],\n",
       "       [720836],\n",
       "       [720867],\n",
       "       [720897],\n",
       "       [720928],\n",
       "       [720958],\n",
       "       [720989],\n",
       "       [721020],\n",
       "       [721048],\n",
       "       [721079],\n",
       "       [721109],\n",
       "       [721140],\n",
       "       [721170],\n",
       "       [721201],\n",
       "       [721232],\n",
       "       [721262],\n",
       "       [721293],\n",
       "       [721323],\n",
       "       [721354],\n",
       "       [721385],\n",
       "       [721414],\n",
       "       [721445],\n",
       "       [721475],\n",
       "       [721506],\n",
       "       [721536],\n",
       "       [721567],\n",
       "       [721598],\n",
       "       [721628],\n",
       "       [721659],\n",
       "       [721689],\n",
       "       [721720],\n",
       "       [721751],\n",
       "       [721779],\n",
       "       [721810],\n",
       "       [721840],\n",
       "       [721871],\n",
       "       [721901],\n",
       "       [721932],\n",
       "       [721963],\n",
       "       [721993],\n",
       "       [722024],\n",
       "       [722054],\n",
       "       [722085],\n",
       "       [722116],\n",
       "       [722144],\n",
       "       [722175],\n",
       "       [722205],\n",
       "       [722236],\n",
       "       [722266],\n",
       "       [722297],\n",
       "       [722328],\n",
       "       [722358],\n",
       "       [722389],\n",
       "       [722419],\n",
       "       [722450],\n",
       "       [722481],\n",
       "       [722509],\n",
       "       [722540],\n",
       "       [722570],\n",
       "       [722601],\n",
       "       [722631],\n",
       "       [722662],\n",
       "       [722693],\n",
       "       [722723],\n",
       "       [722754],\n",
       "       [722784],\n",
       "       [722815],\n",
       "       [722846],\n",
       "       [722875],\n",
       "       [722906],\n",
       "       [722936],\n",
       "       [722967],\n",
       "       [722997],\n",
       "       [723028],\n",
       "       [723059],\n",
       "       [723089],\n",
       "       [723120],\n",
       "       [723150],\n",
       "       [723181],\n",
       "       [723212],\n",
       "       [723240],\n",
       "       [723271],\n",
       "       [723301],\n",
       "       [723332],\n",
       "       [723362],\n",
       "       [723393],\n",
       "       [723424],\n",
       "       [723454],\n",
       "       [723485],\n",
       "       [723515],\n",
       "       [723546],\n",
       "       [723577],\n",
       "       [723605],\n",
       "       [723636],\n",
       "       [723666],\n",
       "       [723697],\n",
       "       [723727],\n",
       "       [723758],\n",
       "       [723789],\n",
       "       [723819],\n",
       "       [723850],\n",
       "       [723880],\n",
       "       [723911],\n",
       "       [723942],\n",
       "       [723970],\n",
       "       [724001],\n",
       "       [724031],\n",
       "       [724062],\n",
       "       [724092],\n",
       "       [724123],\n",
       "       [724154],\n",
       "       [724184],\n",
       "       [724215],\n",
       "       [724245],\n",
       "       [724276],\n",
       "       [724307],\n",
       "       [724336],\n",
       "       [724367],\n",
       "       [724397],\n",
       "       [724428],\n",
       "       [724458],\n",
       "       [724489],\n",
       "       [724520],\n",
       "       [724550],\n",
       "       [724581],\n",
       "       [724611],\n",
       "       [724642],\n",
       "       [724673],\n",
       "       [724701],\n",
       "       [724732],\n",
       "       [724762],\n",
       "       [724793],\n",
       "       [724823],\n",
       "       [724854],\n",
       "       [724885],\n",
       "       [724915],\n",
       "       [724946],\n",
       "       [724976],\n",
       "       [725007],\n",
       "       [725038],\n",
       "       [725066],\n",
       "       [725097],\n",
       "       [725127],\n",
       "       [725158],\n",
       "       [725188],\n",
       "       [725219],\n",
       "       [725250],\n",
       "       [725280],\n",
       "       [725311],\n",
       "       [725341],\n",
       "       [725372],\n",
       "       [725403],\n",
       "       [725431],\n",
       "       [725462],\n",
       "       [725492],\n",
       "       [725523],\n",
       "       [725553],\n",
       "       [725584],\n",
       "       [725615],\n",
       "       [725645],\n",
       "       [725676],\n",
       "       [725706],\n",
       "       [725737],\n",
       "       [725768],\n",
       "       [725797],\n",
       "       [725828],\n",
       "       [725858],\n",
       "       [725889],\n",
       "       [725919],\n",
       "       [725950],\n",
       "       [725981],\n",
       "       [726011],\n",
       "       [726042],\n",
       "       [726072],\n",
       "       [726103],\n",
       "       [726134],\n",
       "       [726162],\n",
       "       [726193],\n",
       "       [726223],\n",
       "       [726254],\n",
       "       [726284],\n",
       "       [726315],\n",
       "       [726346],\n",
       "       [726376],\n",
       "       [726407],\n",
       "       [726437],\n",
       "       [726468],\n",
       "       [726499],\n",
       "       [726527],\n",
       "       [726558],\n",
       "       [726588],\n",
       "       [726619],\n",
       "       [726649],\n",
       "       [726680],\n",
       "       [726711],\n",
       "       [726741],\n",
       "       [726772],\n",
       "       [726802],\n",
       "       [726833],\n",
       "       [726864],\n",
       "       [726892],\n",
       "       [726923],\n",
       "       [726953],\n",
       "       [726984],\n",
       "       [727014],\n",
       "       [727045],\n",
       "       [727076],\n",
       "       [727106],\n",
       "       [727137],\n",
       "       [727167],\n",
       "       [727198],\n",
       "       [727229],\n",
       "       [727258],\n",
       "       [727289],\n",
       "       [727319],\n",
       "       [727350],\n",
       "       [727380],\n",
       "       [727411],\n",
       "       [727442],\n",
       "       [727472],\n",
       "       [727503],\n",
       "       [727533],\n",
       "       [727564],\n",
       "       [727595],\n",
       "       [727623],\n",
       "       [727654],\n",
       "       [727684],\n",
       "       [727715],\n",
       "       [727745],\n",
       "       [727776],\n",
       "       [727807],\n",
       "       [727837],\n",
       "       [727868],\n",
       "       [727898],\n",
       "       [727929],\n",
       "       [727960],\n",
       "       [727988],\n",
       "       [728019],\n",
       "       [728049],\n",
       "       [728080],\n",
       "       [728110],\n",
       "       [728141],\n",
       "       [728172],\n",
       "       [728202],\n",
       "       [728233],\n",
       "       [728263],\n",
       "       [728294],\n",
       "       [728325],\n",
       "       [728353],\n",
       "       [728384],\n",
       "       [728414],\n",
       "       [728445],\n",
       "       [728475],\n",
       "       [728506],\n",
       "       [728537],\n",
       "       [728567],\n",
       "       [728598],\n",
       "       [728628],\n",
       "       [728659],\n",
       "       [728690],\n",
       "       [728719],\n",
       "       [728750],\n",
       "       [728780],\n",
       "       [728811],\n",
       "       [728841],\n",
       "       [728872],\n",
       "       [728903],\n",
       "       [728933],\n",
       "       [728964],\n",
       "       [728994],\n",
       "       [729025],\n",
       "       [729056],\n",
       "       [729084],\n",
       "       [729115],\n",
       "       [729145],\n",
       "       [729176],\n",
       "       [729206],\n",
       "       [729237],\n",
       "       [729268],\n",
       "       [729298],\n",
       "       [729329],\n",
       "       [729359],\n",
       "       [729390],\n",
       "       [729421],\n",
       "       [729449],\n",
       "       [729480],\n",
       "       [729510],\n",
       "       [729541],\n",
       "       [729571],\n",
       "       [729602],\n",
       "       [729633],\n",
       "       [729663],\n",
       "       [729694],\n",
       "       [729724],\n",
       "       [729755],\n",
       "       [729786],\n",
       "       [729814],\n",
       "       [729845],\n",
       "       [729875],\n",
       "       [729906],\n",
       "       [729936],\n",
       "       [729967],\n",
       "       [729998],\n",
       "       [730028],\n",
       "       [730059],\n",
       "       [730089],\n",
       "       [730120],\n",
       "       [730151],\n",
       "       [730180],\n",
       "       [730211],\n",
       "       [730241],\n",
       "       [730272],\n",
       "       [730302],\n",
       "       [730333],\n",
       "       [730364],\n",
       "       [730394],\n",
       "       [730425],\n",
       "       [730455],\n",
       "       [730486],\n",
       "       [730517],\n",
       "       [730545],\n",
       "       [730576],\n",
       "       [730606],\n",
       "       [730637],\n",
       "       [730667],\n",
       "       [730698],\n",
       "       [730729],\n",
       "       [730759],\n",
       "       [730790],\n",
       "       [730820],\n",
       "       [730851],\n",
       "       [730882],\n",
       "       [730910],\n",
       "       [730941],\n",
       "       [730971],\n",
       "       [731002],\n",
       "       [731032],\n",
       "       [731063],\n",
       "       [731094],\n",
       "       [731124],\n",
       "       [731155],\n",
       "       [731185],\n",
       "       [731216],\n",
       "       [731247],\n",
       "       [731275],\n",
       "       [731306],\n",
       "       [731336],\n",
       "       [731367],\n",
       "       [731397],\n",
       "       [731428],\n",
       "       [731459],\n",
       "       [731489],\n",
       "       [731520],\n",
       "       [731550],\n",
       "       [731581],\n",
       "       [731612],\n",
       "       [731641],\n",
       "       [731672],\n",
       "       [731702],\n",
       "       [731733],\n",
       "       [731763],\n",
       "       [731794],\n",
       "       [731825],\n",
       "       [731855],\n",
       "       [731886],\n",
       "       [731916],\n",
       "       [731947],\n",
       "       [731978],\n",
       "       [732006],\n",
       "       [732037],\n",
       "       [732067],\n",
       "       [732098],\n",
       "       [732128],\n",
       "       [732159],\n",
       "       [732190],\n",
       "       [732220],\n",
       "       [732251],\n",
       "       [732281],\n",
       "       [732312],\n",
       "       [732343],\n",
       "       [732371],\n",
       "       [732402],\n",
       "       [732432],\n",
       "       [732463],\n",
       "       [732493],\n",
       "       [732524],\n",
       "       [732555],\n",
       "       [732585],\n",
       "       [732616],\n",
       "       [732646],\n",
       "       [732677],\n",
       "       [732708],\n",
       "       [732736],\n",
       "       [732767],\n",
       "       [732797],\n",
       "       [732828],\n",
       "       [732858],\n",
       "       [732889],\n",
       "       [732920],\n",
       "       [732950],\n",
       "       [732981],\n",
       "       [733011],\n",
       "       [733042],\n",
       "       [733073],\n",
       "       [733102],\n",
       "       [733133],\n",
       "       [733163],\n",
       "       [733194],\n",
       "       [733224],\n",
       "       [733255],\n",
       "       [733286],\n",
       "       [733316],\n",
       "       [733347],\n",
       "       [733377],\n",
       "       [733408],\n",
       "       [733439],\n",
       "       [733467],\n",
       "       [733498],\n",
       "       [733528],\n",
       "       [733559],\n",
       "       [733589],\n",
       "       [733620],\n",
       "       [733651],\n",
       "       [733681],\n",
       "       [733712],\n",
       "       [733742],\n",
       "       [733773],\n",
       "       [733804],\n",
       "       [733832],\n",
       "       [733863],\n",
       "       [733893],\n",
       "       [733924],\n",
       "       [733954],\n",
       "       [733985],\n",
       "       [734016],\n",
       "       [734046],\n",
       "       [734077],\n",
       "       [734107],\n",
       "       [734138],\n",
       "       [734169],\n",
       "       [734197],\n",
       "       [734228],\n",
       "       [734258],\n",
       "       [734289],\n",
       "       [734319],\n",
       "       [734350],\n",
       "       [734381],\n",
       "       [734411],\n",
       "       [734442],\n",
       "       [734472],\n",
       "       [734503],\n",
       "       [734534],\n",
       "       [734563],\n",
       "       [734594],\n",
       "       [734624],\n",
       "       [734655],\n",
       "       [734685],\n",
       "       [734716],\n",
       "       [734747],\n",
       "       [734777],\n",
       "       [734808],\n",
       "       [734838],\n",
       "       [734869],\n",
       "       [734900],\n",
       "       [734928],\n",
       "       [734959],\n",
       "       [734989],\n",
       "       [735020],\n",
       "       [735050],\n",
       "       [735081],\n",
       "       [735112],\n",
       "       [735142],\n",
       "       [735173],\n",
       "       [735203],\n",
       "       [735234],\n",
       "       [735265],\n",
       "       [735293],\n",
       "       [735324],\n",
       "       [735354],\n",
       "       [735385],\n",
       "       [735415],\n",
       "       [735446],\n",
       "       [735477],\n",
       "       [735507],\n",
       "       [735538],\n",
       "       [735568],\n",
       "       [735599],\n",
       "       [735630],\n",
       "       [735658],\n",
       "       [735689],\n",
       "       [735719],\n",
       "       [735750],\n",
       "       [735780],\n",
       "       [735811],\n",
       "       [735842],\n",
       "       [735872],\n",
       "       [735903],\n",
       "       [735933],\n",
       "       [735964],\n",
       "       [735995],\n",
       "       [736024],\n",
       "       [736055],\n",
       "       [736085],\n",
       "       [736116],\n",
       "       [736146],\n",
       "       [736177],\n",
       "       [736208],\n",
       "       [736238],\n",
       "       [736269],\n",
       "       [736299],\n",
       "       [736330],\n",
       "       [736361],\n",
       "       [736389],\n",
       "       [736420],\n",
       "       [736450],\n",
       "       [736481],\n",
       "       [736511],\n",
       "       [736542],\n",
       "       [736573],\n",
       "       [736603],\n",
       "       [736664]], dtype=int64)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  x axis \n",
    "X = production.year_month.values.reshape(-1, 1)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0.000000\n",
       "1      0.000000\n",
       "2      0.000000\n",
       "3      0.000000\n",
       "4      0.000000\n",
       "         ...   \n",
       "810    0.228992\n",
       "811    0.429702\n",
       "812    0.305556\n",
       "813    0.200000\n",
       "814    0.285714\n",
       "Name: percent_female, Length: 815, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = production.percent_female\n",
    "\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_prod = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_prod.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(815,)\n",
      "815\n"
     ]
    }
   ],
   "source": [
    "yp_pred = model_prod.predict(X)\n",
    "print(yp_pred.shape)\n",
    "print(len(yp_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1950-01\n",
       "1      1950-02\n",
       "2      1950-03\n",
       "3      1950-04\n",
       "4      1950-05\n",
       "        ...   \n",
       "810    2017-07\n",
       "811    2017-08\n",
       "812    2017-09\n",
       "813    2017-10\n",
       "814    2017-12\n",
       "Length: 815, dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_transform = pd.Series(list(X))\n",
    "X_transform = X_transform.apply(dt.datetime.fromordinal)\n",
    "X_transform = pd.to_datetime(X_transform).dt.strftime('%Y-%m')\n",
    "\n",
    "X_transform"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Production Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 900x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9,9))\n",
    "\n",
    "plt.scatter(X_transform, y)\n",
    "plt.plot(X_transform, yp_pred, color='red')\n",
    "plt.xlabel('Release Date')\n",
    "plt.ylabel('Percentage of Female Staff (Production)')\n",
    "\n",
    "plt.xticks(np.arange(0, len(X_transform)+1, 60),rotation=45)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Production linear regression equation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Weight coefficients:  [1.54283798e-05]\n",
      "y-axis intercept:  -11.015438277311976\n"
     ]
    }
   ],
   "source": [
    "print('Weight coefficients: ', model_prod.coef_)\n",
    "print('y-axis intercept: ', model_prod.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year_month</th>\n",
       "      <th>percent_female</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>year_month</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.857818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>percent_female</th>\n",
       "      <td>0.857818</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                year_month  percent_female\n",
       "year_month        1.000000        0.857818\n",
       "percent_female    0.857818        1.000000"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "production.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_year_month(slope, intercept, y_value):\n",
    "    return (y_value- intercept)/slope\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When would we have a 50% of female participation in the Production department according to the linear regression model?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[746380.27115613]\n"
     ]
    }
   ],
   "source": [
    "production_fiftypercent = calc_year_month(model_prod.coef_,model_prod.intercept_, 0.5)\n",
    "\n",
    "print(production_fiftypercent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2044-07'"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert ordinal number to readable format\n",
    "# fromordinal needs an int instead of a float\n",
    "# use following solution but convert production_fiftypercent to int inside function\n",
    "# https://stackoverflow.com/questions/16542074/what-is-the-inverse-of-date-toordinal-in-python\n",
    "production_fifty_conversion = datetime.fromordinal(int(production_fiftypercent)).strftime('%Y-%m')\n",
    "\n",
    "production_fifty_conversion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Production Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions_prod = model_prod.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# error delete prediction vs actual data\n",
    "error_prod = predictions_prod - y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Predicted</th>\n",
       "      <th>Actual</th>\n",
       "      <th>Error</th>\n",
       "      <th>MSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.032623</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.032623</td>\n",
       "      <td>0.001064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.032144</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.032144</td>\n",
       "      <td>0.001033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.031712</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.031712</td>\n",
       "      <td>0.001006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.031234</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.031234</td>\n",
       "      <td>0.000976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.030771</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.030771</td>\n",
       "      <td>0.000947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>810</th>\n",
       "      <td>0.347733</td>\n",
       "      <td>0.228992</td>\n",
       "      <td>0.118742</td>\n",
       "      <td>0.014100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>811</th>\n",
       "      <td>0.348211</td>\n",
       "      <td>0.429702</td>\n",
       "      <td>-0.081490</td>\n",
       "      <td>0.006641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>812</th>\n",
       "      <td>0.348690</td>\n",
       "      <td>0.305556</td>\n",
       "      <td>0.043134</td>\n",
       "      <td>0.001861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>813</th>\n",
       "      <td>0.349153</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.149153</td>\n",
       "      <td>0.022246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>814</th>\n",
       "      <td>0.350094</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.064379</td>\n",
       "      <td>0.004145</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>815 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Predicted    Actual     Error       MSE\n",
       "0    -0.032623  0.000000 -0.032623  0.001064\n",
       "1    -0.032144  0.000000 -0.032144  0.001033\n",
       "2    -0.031712  0.000000 -0.031712  0.001006\n",
       "3    -0.031234  0.000000 -0.031234  0.000976\n",
       "4    -0.030771  0.000000 -0.030771  0.000947\n",
       "..         ...       ...       ...       ...\n",
       "810   0.347733  0.228992  0.118742  0.014100\n",
       "811   0.348211  0.429702 -0.081490  0.006641\n",
       "812   0.348690  0.305556  0.043134  0.001861\n",
       "813   0.349153  0.200000  0.149153  0.022246\n",
       "814   0.350094  0.285714  0.064379  0.004145\n",
       "\n",
       "[815 rows x 4 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "production_error_df = pd.DataFrame({\"Predicted\": predictions_prod, \"Actual\": y, \"Error\": error_prod, 'MSE': error_prod**2})[[\"Predicted\", \"Actual\", \"Error\",\"MSE\"]]\n",
    "\n",
    "production_error_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Predicted</th>\n",
       "      <th>Actual</th>\n",
       "      <th>Error</th>\n",
       "      <th>MSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>815.000000</td>\n",
       "      <td>815.000000</td>\n",
       "      <td>8.150000e+02</td>\n",
       "      <td>8.150000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.158493</td>\n",
       "      <td>0.158493</td>\n",
       "      <td>-6.302389e-16</td>\n",
       "      <td>4.381842e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.110552</td>\n",
       "      <td>0.128876</td>\n",
       "      <td>6.623613e-02</td>\n",
       "      <td>6.845000e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.032623</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-2.602119e-01</td>\n",
       "      <td>1.202361e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.062933</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-3.479579e-02</td>\n",
       "      <td>3.098932e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.158489</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>3.648291e-03</td>\n",
       "      <td>1.725697e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.254067</td>\n",
       "      <td>0.268915</td>\n",
       "      <td>4.662550e-02</td>\n",
       "      <td>5.328509e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.350094</td>\n",
       "      <td>0.466013</td>\n",
       "      <td>1.820802e-01</td>\n",
       "      <td>6.771024e-02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Predicted      Actual         Error           MSE\n",
       "count  815.000000  815.000000  8.150000e+02  8.150000e+02\n",
       "mean     0.158493    0.158493 -6.302389e-16  4.381842e-03\n",
       "std      0.110552    0.128876  6.623613e-02  6.845000e-03\n",
       "min     -0.032623    0.000000 -2.602119e-01  1.202361e-08\n",
       "25%      0.062933    0.000000 -3.479579e-02  3.098932e-04\n",
       "50%      0.158489    0.166667  3.648291e-03  1.725697e-03\n",
       "75%      0.254067    0.268915  4.662550e-02  5.328509e-03\n",
       "max      0.350094    0.466013  1.820802e-01  6.771024e-02"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "production_error_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Residuals graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Residuals\n",
    "plt.scatter(predictions_prod, error_prod)\n",
    "plt.hlines(y=0, xmin=predictions_prod.min(), xmax=predictions_prod.max(), colors=\"red\")\n",
    "plt.title(label=\"Residuals plot for Production department\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Writing\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_id</th>\n",
       "      <th>release_date</th>\n",
       "      <th>percent_female</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11006</th>\n",
       "      <td>83015</td>\n",
       "      <td>1950-01-01</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1828</th>\n",
       "      <td>43391</td>\n",
       "      <td>1950-01-04</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17484</th>\n",
       "      <td>65586</td>\n",
       "      <td>1950-01-06</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12152</th>\n",
       "      <td>20088</td>\n",
       "      <td>1950-01-12</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13721</th>\n",
       "      <td>151310</td>\n",
       "      <td>1950-01-13</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20552</th>\n",
       "      <td>428493</td>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19673</th>\n",
       "      <td>374471</td>\n",
       "      <td>2017-09-14</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21321</th>\n",
       "      <td>439502</td>\n",
       "      <td>2017-09-22</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20454</th>\n",
       "      <td>398818</td>\n",
       "      <td>2017-10-27</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21178</th>\n",
       "      <td>341689</td>\n",
       "      <td>2017-12-27</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21558 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       movie_id release_date  percent_female\n",
       "11006     83015   1950-01-01             0.0\n",
       "1828      43391   1950-01-04             0.0\n",
       "17484     65586   1950-01-06             0.0\n",
       "12152     20088   1950-01-12             0.0\n",
       "13721    151310   1950-01-13             1.0\n",
       "...         ...          ...             ...\n",
       "20552    428493   2017-09-01             0.0\n",
       "19673    374471   2017-09-14             0.0\n",
       "21321    439502   2017-09-22             0.0\n",
       "20454    398818   2017-10-27             0.0\n",
       "21178    341689   2017-12-27             0.0\n",
       "\n",
       "[21558 rows x 3 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "writing_df = pd.read_csv(\"Resources/percent_writing.csv\")\n",
    "writing_df = writing_df.sort_values('release_date')\n",
    "\n",
    "writing_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movie_id                   int64\n",
       "release_date      datetime64[ns]\n",
       "percent_female           float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# change release_date column from string to datetime\n",
    "writing_df['release_date'] = pd.to_datetime(writing_df['release_date'])\n",
    "\n",
    "writing_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_id</th>\n",
       "      <th>release_date</th>\n",
       "      <th>percent_female</th>\n",
       "      <th>year_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11006</th>\n",
       "      <td>83015</td>\n",
       "      <td>1950-01-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1950-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1828</th>\n",
       "      <td>43391</td>\n",
       "      <td>1950-01-04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1950-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17484</th>\n",
       "      <td>65586</td>\n",
       "      <td>1950-01-06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1950-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12152</th>\n",
       "      <td>20088</td>\n",
       "      <td>1950-01-12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1950-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13721</th>\n",
       "      <td>151310</td>\n",
       "      <td>1950-01-13</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1950-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20552</th>\n",
       "      <td>428493</td>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19673</th>\n",
       "      <td>374471</td>\n",
       "      <td>2017-09-14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21321</th>\n",
       "      <td>439502</td>\n",
       "      <td>2017-09-22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20454</th>\n",
       "      <td>398818</td>\n",
       "      <td>2017-10-27</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21178</th>\n",
       "      <td>341689</td>\n",
       "      <td>2017-12-27</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21558 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       movie_id release_date  percent_female year_month\n",
       "11006     83015   1950-01-01             0.0    1950-01\n",
       "1828      43391   1950-01-04             0.0    1950-01\n",
       "17484     65586   1950-01-06             0.0    1950-01\n",
       "12152     20088   1950-01-12             0.0    1950-01\n",
       "13721    151310   1950-01-13             1.0    1950-01\n",
       "...         ...          ...             ...        ...\n",
       "20552    428493   2017-09-01             0.0    2017-09\n",
       "19673    374471   2017-09-14             0.0    2017-09\n",
       "21321    439502   2017-09-22             0.0    2017-09\n",
       "20454    398818   2017-10-27             0.0    2017-10\n",
       "21178    341689   2017-12-27             0.0    2017-12\n",
       "\n",
       "[21558 rows x 4 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use Pandas.to_datetime() and datetime.strftime() method to add year_month column\n",
    "writing_df['year_month'] = pd.to_datetime(writing_df['release_date']).dt.strftime('%Y-%m')\n",
    "\n",
    "writing_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movie_id                   int64\n",
       "release_date      datetime64[ns]\n",
       "percent_female           float64\n",
       "year_month        datetime64[ns]\n",
       "dtype: object"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert year_month column to datetime format\n",
    "writing_df['year_month'] = pd.to_datetime(writing_df['year_month'])\n",
    "\n",
    "writing_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movie_id                   int64\n",
       "release_date      datetime64[ns]\n",
       "percent_female           float64\n",
       "year_month                 int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert year_month to ordinal for ML\n",
    "writing_df['year_month']=writing_df['year_month'].map(dt.datetime.toordinal)\n",
    "\n",
    "writing_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year_month\n",
       "711858    0.125000\n",
       "711889    0.060606\n",
       "711917    0.027778\n",
       "711948    0.000000\n",
       "711978    0.125000\n",
       "            ...   \n",
       "736511    0.312500\n",
       "736542    0.062500\n",
       "736573    0.000000\n",
       "736603    0.000000\n",
       "736664    0.000000\n",
       "Name: percent_female, Length: 815, dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "writ_count = writing_df.groupby(['year_month'])['percent_female'].mean()\n",
    "\n",
    "writ_count"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DONT RERUN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year_month</th>\n",
       "      <th>percent_female</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>711858</td>\n",
       "      <td>0.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>711889</td>\n",
       "      <td>0.060606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>711917</td>\n",
       "      <td>0.027778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>711948</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>711978</td>\n",
       "      <td>0.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>810</th>\n",
       "      <td>736511</td>\n",
       "      <td>0.312500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>811</th>\n",
       "      <td>736542</td>\n",
       "      <td>0.062500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>812</th>\n",
       "      <td>736573</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>813</th>\n",
       "      <td>736603</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>814</th>\n",
       "      <td>736664</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>815 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     year_month  percent_female\n",
       "0        711858        0.125000\n",
       "1        711889        0.060606\n",
       "2        711917        0.027778\n",
       "3        711948        0.000000\n",
       "4        711978        0.125000\n",
       "..          ...             ...\n",
       "810      736511        0.312500\n",
       "811      736542        0.062500\n",
       "812      736573        0.000000\n",
       "813      736603        0.000000\n",
       "814      736664        0.000000\n",
       "\n",
       "[815 rows x 2 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Change from series to df\n",
    "writing = writ_count.to_frame()\n",
    "\n",
    "# Reset index  DO NOT RERUN\n",
    "# Reset index  DO NOT RERUN\n",
    "writing  = writing.rename_axis('year_month').reset_index()\n",
    "\n",
    "# Review df\n",
    "writing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Review data distribution before applying ML model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Preview data\n",
    "plt.scatter(writing.year_month, writing.percent_female)\n",
    "plt.xlabel('Release Date')\n",
    "plt.ylabel('Percentage of Female Writing Staff')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[711858],\n",
       "       [711889],\n",
       "       [711917],\n",
       "       [711948],\n",
       "       [711978],\n",
       "       [712009],\n",
       "       [712039],\n",
       "       [712070],\n",
       "       [712101],\n",
       "       [712131],\n",
       "       [712162],\n",
       "       [712192],\n",
       "       [712223],\n",
       "       [712254],\n",
       "       [712282],\n",
       "       [712313],\n",
       "       [712343],\n",
       "       [712374],\n",
       "       [712404],\n",
       "       [712435],\n",
       "       [712466],\n",
       "       [712496],\n",
       "       [712527],\n",
       "       [712557],\n",
       "       [712588],\n",
       "       [712619],\n",
       "       [712648],\n",
       "       [712679],\n",
       "       [712709],\n",
       "       [712740],\n",
       "       [712770],\n",
       "       [712801],\n",
       "       [712832],\n",
       "       [712862],\n",
       "       [712893],\n",
       "       [712923],\n",
       "       [712954],\n",
       "       [712985],\n",
       "       [713013],\n",
       "       [713044],\n",
       "       [713074],\n",
       "       [713105],\n",
       "       [713135],\n",
       "       [713166],\n",
       "       [713197],\n",
       "       [713227],\n",
       "       [713258],\n",
       "       [713288],\n",
       "       [713319],\n",
       "       [713350],\n",
       "       [713378],\n",
       "       [713409],\n",
       "       [713439],\n",
       "       [713470],\n",
       "       [713500],\n",
       "       [713531],\n",
       "       [713562],\n",
       "       [713592],\n",
       "       [713623],\n",
       "       [713653],\n",
       "       [713684],\n",
       "       [713715],\n",
       "       [713743],\n",
       "       [713774],\n",
       "       [713804],\n",
       "       [713835],\n",
       "       [713865],\n",
       "       [713896],\n",
       "       [713927],\n",
       "       [713957],\n",
       "       [713988],\n",
       "       [714018],\n",
       "       [714049],\n",
       "       [714080],\n",
       "       [714109],\n",
       "       [714140],\n",
       "       [714170],\n",
       "       [714201],\n",
       "       [714231],\n",
       "       [714262],\n",
       "       [714293],\n",
       "       [714323],\n",
       "       [714354],\n",
       "       [714384],\n",
       "       [714415],\n",
       "       [714446],\n",
       "       [714474],\n",
       "       [714505],\n",
       "       [714535],\n",
       "       [714566],\n",
       "       [714596],\n",
       "       [714627],\n",
       "       [714658],\n",
       "       [714688],\n",
       "       [714719],\n",
       "       [714749],\n",
       "       [714780],\n",
       "       [714811],\n",
       "       [714839],\n",
       "       [714870],\n",
       "       [714900],\n",
       "       [714931],\n",
       "       [714961],\n",
       "       [714992],\n",
       "       [715023],\n",
       "       [715053],\n",
       "       [715084],\n",
       "       [715114],\n",
       "       [715145],\n",
       "       [715176],\n",
       "       [715204],\n",
       "       [715235],\n",
       "       [715265],\n",
       "       [715296],\n",
       "       [715326],\n",
       "       [715357],\n",
       "       [715388],\n",
       "       [715418],\n",
       "       [715449],\n",
       "       [715479],\n",
       "       [715510],\n",
       "       [715541],\n",
       "       [715570],\n",
       "       [715601],\n",
       "       [715631],\n",
       "       [715662],\n",
       "       [715692],\n",
       "       [715723],\n",
       "       [715754],\n",
       "       [715784],\n",
       "       [715815],\n",
       "       [715845],\n",
       "       [715876],\n",
       "       [715907],\n",
       "       [715935],\n",
       "       [715966],\n",
       "       [715996],\n",
       "       [716027],\n",
       "       [716057],\n",
       "       [716088],\n",
       "       [716119],\n",
       "       [716149],\n",
       "       [716180],\n",
       "       [716210],\n",
       "       [716241],\n",
       "       [716272],\n",
       "       [716300],\n",
       "       [716331],\n",
       "       [716361],\n",
       "       [716392],\n",
       "       [716422],\n",
       "       [716453],\n",
       "       [716484],\n",
       "       [716514],\n",
       "       [716545],\n",
       "       [716575],\n",
       "       [716606],\n",
       "       [716637],\n",
       "       [716665],\n",
       "       [716696],\n",
       "       [716726],\n",
       "       [716757],\n",
       "       [716787],\n",
       "       [716818],\n",
       "       [716849],\n",
       "       [716879],\n",
       "       [716910],\n",
       "       [716940],\n",
       "       [716971],\n",
       "       [717002],\n",
       "       [717031],\n",
       "       [717062],\n",
       "       [717092],\n",
       "       [717123],\n",
       "       [717153],\n",
       "       [717184],\n",
       "       [717215],\n",
       "       [717245],\n",
       "       [717276],\n",
       "       [717306],\n",
       "       [717337],\n",
       "       [717368],\n",
       "       [717396],\n",
       "       [717427],\n",
       "       [717457],\n",
       "       [717488],\n",
       "       [717518],\n",
       "       [717549],\n",
       "       [717580],\n",
       "       [717610],\n",
       "       [717641],\n",
       "       [717671],\n",
       "       [717702],\n",
       "       [717733],\n",
       "       [717761],\n",
       "       [717792],\n",
       "       [717822],\n",
       "       [717853],\n",
       "       [717883],\n",
       "       [717914],\n",
       "       [717945],\n",
       "       [717975],\n",
       "       [718006],\n",
       "       [718036],\n",
       "       [718067],\n",
       "       [718098],\n",
       "       [718126],\n",
       "       [718157],\n",
       "       [718187],\n",
       "       [718218],\n",
       "       [718248],\n",
       "       [718279],\n",
       "       [718310],\n",
       "       [718340],\n",
       "       [718371],\n",
       "       [718401],\n",
       "       [718432],\n",
       "       [718463],\n",
       "       [718492],\n",
       "       [718523],\n",
       "       [718553],\n",
       "       [718584],\n",
       "       [718614],\n",
       "       [718645],\n",
       "       [718676],\n",
       "       [718706],\n",
       "       [718737],\n",
       "       [718767],\n",
       "       [718798],\n",
       "       [718829],\n",
       "       [718857],\n",
       "       [718888],\n",
       "       [718918],\n",
       "       [718949],\n",
       "       [718979],\n",
       "       [719010],\n",
       "       [719041],\n",
       "       [719071],\n",
       "       [719102],\n",
       "       [719132],\n",
       "       [719163],\n",
       "       [719194],\n",
       "       [719222],\n",
       "       [719253],\n",
       "       [719283],\n",
       "       [719314],\n",
       "       [719344],\n",
       "       [719375],\n",
       "       [719406],\n",
       "       [719436],\n",
       "       [719467],\n",
       "       [719497],\n",
       "       [719528],\n",
       "       [719559],\n",
       "       [719587],\n",
       "       [719618],\n",
       "       [719648],\n",
       "       [719679],\n",
       "       [719709],\n",
       "       [719740],\n",
       "       [719771],\n",
       "       [719801],\n",
       "       [719832],\n",
       "       [719862],\n",
       "       [719893],\n",
       "       [719924],\n",
       "       [719953],\n",
       "       [719984],\n",
       "       [720014],\n",
       "       [720045],\n",
       "       [720075],\n",
       "       [720106],\n",
       "       [720137],\n",
       "       [720167],\n",
       "       [720198],\n",
       "       [720228],\n",
       "       [720259],\n",
       "       [720290],\n",
       "       [720318],\n",
       "       [720349],\n",
       "       [720379],\n",
       "       [720410],\n",
       "       [720440],\n",
       "       [720471],\n",
       "       [720502],\n",
       "       [720532],\n",
       "       [720563],\n",
       "       [720593],\n",
       "       [720624],\n",
       "       [720655],\n",
       "       [720683],\n",
       "       [720714],\n",
       "       [720744],\n",
       "       [720775],\n",
       "       [720805],\n",
       "       [720836],\n",
       "       [720867],\n",
       "       [720897],\n",
       "       [720928],\n",
       "       [720958],\n",
       "       [720989],\n",
       "       [721020],\n",
       "       [721048],\n",
       "       [721079],\n",
       "       [721109],\n",
       "       [721140],\n",
       "       [721170],\n",
       "       [721201],\n",
       "       [721232],\n",
       "       [721262],\n",
       "       [721293],\n",
       "       [721323],\n",
       "       [721354],\n",
       "       [721385],\n",
       "       [721414],\n",
       "       [721445],\n",
       "       [721475],\n",
       "       [721506],\n",
       "       [721536],\n",
       "       [721567],\n",
       "       [721598],\n",
       "       [721628],\n",
       "       [721659],\n",
       "       [721689],\n",
       "       [721720],\n",
       "       [721751],\n",
       "       [721779],\n",
       "       [721810],\n",
       "       [721840],\n",
       "       [721871],\n",
       "       [721901],\n",
       "       [721932],\n",
       "       [721963],\n",
       "       [721993],\n",
       "       [722024],\n",
       "       [722054],\n",
       "       [722085],\n",
       "       [722116],\n",
       "       [722144],\n",
       "       [722175],\n",
       "       [722205],\n",
       "       [722236],\n",
       "       [722266],\n",
       "       [722297],\n",
       "       [722328],\n",
       "       [722358],\n",
       "       [722389],\n",
       "       [722419],\n",
       "       [722450],\n",
       "       [722481],\n",
       "       [722509],\n",
       "       [722540],\n",
       "       [722570],\n",
       "       [722601],\n",
       "       [722631],\n",
       "       [722662],\n",
       "       [722693],\n",
       "       [722723],\n",
       "       [722754],\n",
       "       [722784],\n",
       "       [722815],\n",
       "       [722846],\n",
       "       [722875],\n",
       "       [722906],\n",
       "       [722936],\n",
       "       [722967],\n",
       "       [722997],\n",
       "       [723028],\n",
       "       [723059],\n",
       "       [723089],\n",
       "       [723120],\n",
       "       [723150],\n",
       "       [723181],\n",
       "       [723212],\n",
       "       [723240],\n",
       "       [723271],\n",
       "       [723301],\n",
       "       [723332],\n",
       "       [723362],\n",
       "       [723393],\n",
       "       [723424],\n",
       "       [723454],\n",
       "       [723485],\n",
       "       [723515],\n",
       "       [723546],\n",
       "       [723577],\n",
       "       [723605],\n",
       "       [723636],\n",
       "       [723666],\n",
       "       [723697],\n",
       "       [723727],\n",
       "       [723758],\n",
       "       [723789],\n",
       "       [723819],\n",
       "       [723850],\n",
       "       [723880],\n",
       "       [723911],\n",
       "       [723942],\n",
       "       [723970],\n",
       "       [724001],\n",
       "       [724031],\n",
       "       [724062],\n",
       "       [724092],\n",
       "       [724123],\n",
       "       [724154],\n",
       "       [724184],\n",
       "       [724215],\n",
       "       [724245],\n",
       "       [724276],\n",
       "       [724307],\n",
       "       [724336],\n",
       "       [724367],\n",
       "       [724397],\n",
       "       [724428],\n",
       "       [724458],\n",
       "       [724489],\n",
       "       [724520],\n",
       "       [724550],\n",
       "       [724581],\n",
       "       [724611],\n",
       "       [724642],\n",
       "       [724673],\n",
       "       [724701],\n",
       "       [724732],\n",
       "       [724762],\n",
       "       [724793],\n",
       "       [724823],\n",
       "       [724854],\n",
       "       [724885],\n",
       "       [724915],\n",
       "       [724946],\n",
       "       [724976],\n",
       "       [725007],\n",
       "       [725038],\n",
       "       [725066],\n",
       "       [725097],\n",
       "       [725127],\n",
       "       [725158],\n",
       "       [725188],\n",
       "       [725219],\n",
       "       [725250],\n",
       "       [725280],\n",
       "       [725311],\n",
       "       [725341],\n",
       "       [725372],\n",
       "       [725403],\n",
       "       [725431],\n",
       "       [725462],\n",
       "       [725492],\n",
       "       [725523],\n",
       "       [725553],\n",
       "       [725584],\n",
       "       [725615],\n",
       "       [725645],\n",
       "       [725676],\n",
       "       [725706],\n",
       "       [725737],\n",
       "       [725768],\n",
       "       [725797],\n",
       "       [725828],\n",
       "       [725858],\n",
       "       [725889],\n",
       "       [725919],\n",
       "       [725950],\n",
       "       [725981],\n",
       "       [726011],\n",
       "       [726042],\n",
       "       [726072],\n",
       "       [726103],\n",
       "       [726134],\n",
       "       [726162],\n",
       "       [726193],\n",
       "       [726223],\n",
       "       [726254],\n",
       "       [726284],\n",
       "       [726315],\n",
       "       [726346],\n",
       "       [726376],\n",
       "       [726407],\n",
       "       [726437],\n",
       "       [726468],\n",
       "       [726499],\n",
       "       [726527],\n",
       "       [726558],\n",
       "       [726588],\n",
       "       [726619],\n",
       "       [726649],\n",
       "       [726680],\n",
       "       [726711],\n",
       "       [726741],\n",
       "       [726772],\n",
       "       [726802],\n",
       "       [726833],\n",
       "       [726864],\n",
       "       [726892],\n",
       "       [726923],\n",
       "       [726953],\n",
       "       [726984],\n",
       "       [727014],\n",
       "       [727045],\n",
       "       [727076],\n",
       "       [727106],\n",
       "       [727137],\n",
       "       [727167],\n",
       "       [727198],\n",
       "       [727229],\n",
       "       [727258],\n",
       "       [727289],\n",
       "       [727319],\n",
       "       [727350],\n",
       "       [727380],\n",
       "       [727411],\n",
       "       [727442],\n",
       "       [727472],\n",
       "       [727503],\n",
       "       [727533],\n",
       "       [727564],\n",
       "       [727595],\n",
       "       [727623],\n",
       "       [727654],\n",
       "       [727684],\n",
       "       [727715],\n",
       "       [727745],\n",
       "       [727776],\n",
       "       [727807],\n",
       "       [727837],\n",
       "       [727868],\n",
       "       [727898],\n",
       "       [727929],\n",
       "       [727960],\n",
       "       [727988],\n",
       "       [728019],\n",
       "       [728049],\n",
       "       [728080],\n",
       "       [728110],\n",
       "       [728141],\n",
       "       [728172],\n",
       "       [728202],\n",
       "       [728233],\n",
       "       [728263],\n",
       "       [728294],\n",
       "       [728325],\n",
       "       [728353],\n",
       "       [728384],\n",
       "       [728414],\n",
       "       [728445],\n",
       "       [728475],\n",
       "       [728506],\n",
       "       [728537],\n",
       "       [728567],\n",
       "       [728598],\n",
       "       [728628],\n",
       "       [728659],\n",
       "       [728690],\n",
       "       [728719],\n",
       "       [728750],\n",
       "       [728780],\n",
       "       [728811],\n",
       "       [728841],\n",
       "       [728872],\n",
       "       [728903],\n",
       "       [728933],\n",
       "       [728964],\n",
       "       [728994],\n",
       "       [729025],\n",
       "       [729056],\n",
       "       [729084],\n",
       "       [729115],\n",
       "       [729145],\n",
       "       [729176],\n",
       "       [729206],\n",
       "       [729237],\n",
       "       [729268],\n",
       "       [729298],\n",
       "       [729329],\n",
       "       [729359],\n",
       "       [729390],\n",
       "       [729421],\n",
       "       [729449],\n",
       "       [729480],\n",
       "       [729510],\n",
       "       [729541],\n",
       "       [729571],\n",
       "       [729602],\n",
       "       [729633],\n",
       "       [729663],\n",
       "       [729694],\n",
       "       [729724],\n",
       "       [729755],\n",
       "       [729786],\n",
       "       [729814],\n",
       "       [729845],\n",
       "       [729875],\n",
       "       [729906],\n",
       "       [729936],\n",
       "       [729967],\n",
       "       [729998],\n",
       "       [730028],\n",
       "       [730059],\n",
       "       [730089],\n",
       "       [730120],\n",
       "       [730151],\n",
       "       [730180],\n",
       "       [730211],\n",
       "       [730241],\n",
       "       [730272],\n",
       "       [730302],\n",
       "       [730333],\n",
       "       [730364],\n",
       "       [730394],\n",
       "       [730425],\n",
       "       [730455],\n",
       "       [730486],\n",
       "       [730517],\n",
       "       [730545],\n",
       "       [730576],\n",
       "       [730606],\n",
       "       [730637],\n",
       "       [730667],\n",
       "       [730698],\n",
       "       [730729],\n",
       "       [730759],\n",
       "       [730790],\n",
       "       [730820],\n",
       "       [730851],\n",
       "       [730882],\n",
       "       [730910],\n",
       "       [730941],\n",
       "       [730971],\n",
       "       [731002],\n",
       "       [731032],\n",
       "       [731063],\n",
       "       [731094],\n",
       "       [731124],\n",
       "       [731155],\n",
       "       [731185],\n",
       "       [731216],\n",
       "       [731247],\n",
       "       [731275],\n",
       "       [731306],\n",
       "       [731336],\n",
       "       [731367],\n",
       "       [731397],\n",
       "       [731428],\n",
       "       [731459],\n",
       "       [731489],\n",
       "       [731520],\n",
       "       [731550],\n",
       "       [731581],\n",
       "       [731612],\n",
       "       [731641],\n",
       "       [731672],\n",
       "       [731702],\n",
       "       [731733],\n",
       "       [731763],\n",
       "       [731794],\n",
       "       [731825],\n",
       "       [731855],\n",
       "       [731886],\n",
       "       [731916],\n",
       "       [731947],\n",
       "       [731978],\n",
       "       [732006],\n",
       "       [732037],\n",
       "       [732067],\n",
       "       [732098],\n",
       "       [732128],\n",
       "       [732159],\n",
       "       [732190],\n",
       "       [732220],\n",
       "       [732251],\n",
       "       [732281],\n",
       "       [732312],\n",
       "       [732343],\n",
       "       [732371],\n",
       "       [732402],\n",
       "       [732432],\n",
       "       [732463],\n",
       "       [732493],\n",
       "       [732524],\n",
       "       [732555],\n",
       "       [732585],\n",
       "       [732616],\n",
       "       [732646],\n",
       "       [732677],\n",
       "       [732708],\n",
       "       [732736],\n",
       "       [732767],\n",
       "       [732797],\n",
       "       [732828],\n",
       "       [732858],\n",
       "       [732889],\n",
       "       [732920],\n",
       "       [732950],\n",
       "       [732981],\n",
       "       [733011],\n",
       "       [733042],\n",
       "       [733073],\n",
       "       [733102],\n",
       "       [733133],\n",
       "       [733163],\n",
       "       [733194],\n",
       "       [733224],\n",
       "       [733255],\n",
       "       [733286],\n",
       "       [733316],\n",
       "       [733347],\n",
       "       [733377],\n",
       "       [733408],\n",
       "       [733439],\n",
       "       [733467],\n",
       "       [733498],\n",
       "       [733528],\n",
       "       [733559],\n",
       "       [733589],\n",
       "       [733620],\n",
       "       [733651],\n",
       "       [733681],\n",
       "       [733712],\n",
       "       [733742],\n",
       "       [733773],\n",
       "       [733804],\n",
       "       [733832],\n",
       "       [733863],\n",
       "       [733893],\n",
       "       [733924],\n",
       "       [733954],\n",
       "       [733985],\n",
       "       [734016],\n",
       "       [734046],\n",
       "       [734077],\n",
       "       [734107],\n",
       "       [734138],\n",
       "       [734169],\n",
       "       [734197],\n",
       "       [734228],\n",
       "       [734258],\n",
       "       [734289],\n",
       "       [734319],\n",
       "       [734350],\n",
       "       [734381],\n",
       "       [734411],\n",
       "       [734442],\n",
       "       [734472],\n",
       "       [734503],\n",
       "       [734534],\n",
       "       [734563],\n",
       "       [734594],\n",
       "       [734624],\n",
       "       [734655],\n",
       "       [734685],\n",
       "       [734716],\n",
       "       [734747],\n",
       "       [734777],\n",
       "       [734808],\n",
       "       [734838],\n",
       "       [734869],\n",
       "       [734900],\n",
       "       [734928],\n",
       "       [734959],\n",
       "       [734989],\n",
       "       [735020],\n",
       "       [735050],\n",
       "       [735081],\n",
       "       [735112],\n",
       "       [735142],\n",
       "       [735173],\n",
       "       [735203],\n",
       "       [735234],\n",
       "       [735265],\n",
       "       [735293],\n",
       "       [735324],\n",
       "       [735354],\n",
       "       [735385],\n",
       "       [735415],\n",
       "       [735446],\n",
       "       [735477],\n",
       "       [735507],\n",
       "       [735538],\n",
       "       [735568],\n",
       "       [735599],\n",
       "       [735630],\n",
       "       [735658],\n",
       "       [735689],\n",
       "       [735719],\n",
       "       [735750],\n",
       "       [735780],\n",
       "       [735811],\n",
       "       [735842],\n",
       "       [735872],\n",
       "       [735903],\n",
       "       [735933],\n",
       "       [735964],\n",
       "       [735995],\n",
       "       [736024],\n",
       "       [736055],\n",
       "       [736085],\n",
       "       [736116],\n",
       "       [736146],\n",
       "       [736177],\n",
       "       [736208],\n",
       "       [736238],\n",
       "       [736269],\n",
       "       [736299],\n",
       "       [736330],\n",
       "       [736361],\n",
       "       [736389],\n",
       "       [736420],\n",
       "       [736450],\n",
       "       [736481],\n",
       "       [736511],\n",
       "       [736542],\n",
       "       [736573],\n",
       "       [736603],\n",
       "       [736664]], dtype=int64)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reshape values\n",
    "Xw = writing.year_month.values.reshape(-1, 1)\n",
    "\n",
    "Xw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0.125000\n",
       "1      0.060606\n",
       "2      0.027778\n",
       "3      0.000000\n",
       "4      0.125000\n",
       "         ...   \n",
       "810    0.312500\n",
       "811    0.062500\n",
       "812    0.000000\n",
       "813    0.000000\n",
       "814    0.000000\n",
       "Name: percent_female, Length: 815, dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yw = writing.percent_female\n",
    "\n",
    "yw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_writ = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_writ.fit(Xw, yw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(815,)\n",
      "815\n"
     ]
    }
   ],
   "source": [
    "yw_pred = model_writ.predict(Xw)\n",
    "print(yw_pred.shape)\n",
    "print(len(yw_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1950-01\n",
       "1      1950-02\n",
       "2      1950-03\n",
       "3      1950-04\n",
       "4      1950-05\n",
       "        ...   \n",
       "810    2017-07\n",
       "811    2017-08\n",
       "812    2017-09\n",
       "813    2017-10\n",
       "814    2017-12\n",
       "Length: 815, dtype: object"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xw_transform = pd.Series(list(Xw))\n",
    "Xw_transform = Xw_transform.apply(dt.datetime.fromordinal)\n",
    "Xw_transform = pd.to_datetime(Xw_transform).dt.strftime('%Y-%m')\n",
    "\n",
    "Xw_transform"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Writing Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 900x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9,9))\n",
    "\n",
    "plt.scatter(Xw_transform, yw)\n",
    "plt.plot(Xw_transform, yw_pred, color='red')\n",
    "plt.xlabel('Release Date')\n",
    "plt.ylabel('Percentage of Female Staff (Writing)')\n",
    "\n",
    "plt.xticks(np.arange(0, len(Xw_transform)+1, 60),rotation=45)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Writing linear regression equation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Weight coefficients:  [4.71404635e-06]\n",
      "y-axis intercept:  -3.335578804469447\n"
     ]
    }
   ],
   "source": [
    "print('Weight coefficients: ', model_writ.coef_)\n",
    "print('y-axis intercept: ', model_writ.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year_month</th>\n",
       "      <th>percent_female</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>year_month</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.544277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>percent_female</th>\n",
       "      <td>0.544277</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                year_month  percent_female\n",
       "year_month        1.000000        0.544277\n",
       "percent_female    0.544277        1.000000"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "writing.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Correlation of 0.53 shows no significant association between time and female participation in the writing department"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When would we have a 50% of female participation in the Writing department according to the linear regression model?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[813648.93709867]\n"
     ]
    }
   ],
   "source": [
    "writing_fiftypercent = calc_year_month(model_writ.coef_,model_writ.intercept_, 0.5)\n",
    "print(writing_fiftypercent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2228-09'"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert ordinal number to readable format\n",
    "# fromordinal needs an int instead of a float\n",
    "# use following solution but convert writing_fiftypercent to int inside function\n",
    "# https://stackoverflow.com/questions/16542074/what-is-the-inverse-of-date-toordinal-in-python\n",
    "writing_fifty_conversion = datetime.fromordinal(int(writing_fiftypercent)).strftime('%Y-%m')\n",
    "\n",
    "writing_fifty_conversion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Writing accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions_writ = model_writ.predict(Xw)\n",
    "# error delete prediction vs actual data\n",
    "error_writ = yw - predictions_writ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Predicted</th>\n",
       "      <th>Actual</th>\n",
       "      <th>Error</th>\n",
       "      <th>MSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.020153</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.104847</td>\n",
       "      <td>0.010993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.020299</td>\n",
       "      <td>0.060606</td>\n",
       "      <td>0.040307</td>\n",
       "      <td>0.001625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.020431</td>\n",
       "      <td>0.027778</td>\n",
       "      <td>0.007347</td>\n",
       "      <td>0.000054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.020577</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.020577</td>\n",
       "      <td>0.000423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.020718</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.104282</td>\n",
       "      <td>0.010875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>810</th>\n",
       "      <td>0.136368</td>\n",
       "      <td>0.312500</td>\n",
       "      <td>0.176132</td>\n",
       "      <td>0.031022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>811</th>\n",
       "      <td>0.136514</td>\n",
       "      <td>0.062500</td>\n",
       "      <td>-0.074014</td>\n",
       "      <td>0.005478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>812</th>\n",
       "      <td>0.136660</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.136660</td>\n",
       "      <td>0.018676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>813</th>\n",
       "      <td>0.136802</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.136802</td>\n",
       "      <td>0.018715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>814</th>\n",
       "      <td>0.137089</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.137089</td>\n",
       "      <td>0.018794</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>815 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Predicted    Actual     Error       MSE\n",
       "0     0.020153  0.125000  0.104847  0.010993\n",
       "1     0.020299  0.060606  0.040307  0.001625\n",
       "2     0.020431  0.027778  0.007347  0.000054\n",
       "3     0.020577  0.000000 -0.020577  0.000423\n",
       "4     0.020718  0.125000  0.104282  0.010875\n",
       "..         ...       ...       ...       ...\n",
       "810   0.136368  0.312500  0.176132  0.031022\n",
       "811   0.136514  0.062500 -0.074014  0.005478\n",
       "812   0.136660  0.000000 -0.136660  0.018676\n",
       "813   0.136802  0.000000 -0.136802  0.018715\n",
       "814   0.137089  0.000000 -0.137089  0.018794\n",
       "\n",
       "[815 rows x 4 columns]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "writing_error_df = pd.DataFrame({\"Predicted\": predictions_writ, \"Actual\": yw, \"Error\": error_writ, \"MSE\": error_writ**2})[[\"Predicted\", \"Actual\", \"Error\", \"MSE\"]]\n",
    "\n",
    "writing_error_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Predicted</th>\n",
       "      <th>Actual</th>\n",
       "      <th>Error</th>\n",
       "      <th>MSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>815.000000</td>\n",
       "      <td>815.000000</td>\n",
       "      <td>8.150000e+02</td>\n",
       "      <td>8.150000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.078547</td>\n",
       "      <td>0.078547</td>\n",
       "      <td>-2.356670e-17</td>\n",
       "      <td>2.707280e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.033779</td>\n",
       "      <td>0.062061</td>\n",
       "      <td>5.206348e-02</td>\n",
       "      <td>4.771151e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.020153</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.370894e-01</td>\n",
       "      <td>2.903145e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.049349</td>\n",
       "      <td>0.025641</td>\n",
       "      <td>-3.822938e-02</td>\n",
       "      <td>3.035528e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.078546</td>\n",
       "      <td>0.076923</td>\n",
       "      <td>-5.428221e-03</td>\n",
       "      <td>1.242930e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.107749</td>\n",
       "      <td>0.118168</td>\n",
       "      <td>2.953946e-02</td>\n",
       "      <td>3.173445e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.137089</td>\n",
       "      <td>0.358974</td>\n",
       "      <td>2.774117e-01</td>\n",
       "      <td>7.695724e-02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Predicted      Actual         Error           MSE\n",
       "count  815.000000  815.000000  8.150000e+02  8.150000e+02\n",
       "mean     0.078547    0.078547 -2.356670e-17  2.707280e-03\n",
       "std      0.033779    0.062061  5.206348e-02  4.771151e-03\n",
       "min      0.020153    0.000000 -1.370894e-01  2.903145e-09\n",
       "25%      0.049349    0.025641 -3.822938e-02  3.035528e-04\n",
       "50%      0.078546    0.076923 -5.428221e-03  1.242930e-03\n",
       "75%      0.107749    0.118168  2.953946e-02  3.173445e-03\n",
       "max      0.137089    0.358974  2.774117e-01  7.695724e-02"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "writing_error_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Residuals graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Residuals\n",
    "plt.scatter(predictions_writ, error_writ)\n",
    "plt.hlines(y=0, xmin=predictions_writ.min(), xmax=predictions_writ.max(),colors=\"red\")\n",
    "plt.title(label=\"Residuals plot for Writing department\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Directing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load file\n",
    "directing_df = pd.read_csv(\"Resources/percent_directing.csv\")\n",
    "directing_df = directing_df.sort_values('release_date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movie_id                   int64\n",
       "release_date      datetime64[ns]\n",
       "percent_female           float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# change release_date column from string to datetime\n",
    "directing_df['release_date'] = pd.to_datetime(directing_df['release_date'])\n",
    "\n",
    "directing_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_id</th>\n",
       "      <th>release_date</th>\n",
       "      <th>percent_female</th>\n",
       "      <th>year_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17465</th>\n",
       "      <td>43395</td>\n",
       "      <td>1950-01-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1950-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15517</th>\n",
       "      <td>43387</td>\n",
       "      <td>1950-01-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1950-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12394</th>\n",
       "      <td>83015</td>\n",
       "      <td>1950-01-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1950-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15847</th>\n",
       "      <td>109364</td>\n",
       "      <td>1950-01-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1950-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>43391</td>\n",
       "      <td>1950-01-04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1950-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24761</th>\n",
       "      <td>426264</td>\n",
       "      <td>2017-08-31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24387</th>\n",
       "      <td>428493</td>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25296</th>\n",
       "      <td>439502</td>\n",
       "      <td>2017-09-22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24275</th>\n",
       "      <td>398818</td>\n",
       "      <td>2017-10-27</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25133</th>\n",
       "      <td>341689</td>\n",
       "      <td>2017-12-27</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25569 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       movie_id release_date  percent_female year_month\n",
       "17465     43395   1950-01-01             0.0    1950-01\n",
       "15517     43387   1950-01-01             0.0    1950-01\n",
       "12394     83015   1950-01-01             0.0    1950-01\n",
       "15847    109364   1950-01-01             0.0    1950-01\n",
       "2007      43391   1950-01-04             0.0    1950-01\n",
       "...         ...          ...             ...        ...\n",
       "24761    426264   2017-08-31             0.0    2017-08\n",
       "24387    428493   2017-09-01             0.0    2017-09\n",
       "25296    439502   2017-09-22             0.0    2017-09\n",
       "24275    398818   2017-10-27             0.0    2017-10\n",
       "25133    341689   2017-12-27             0.0    2017-12\n",
       "\n",
       "[25569 rows x 4 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use Pandas.to_datetime() and datetime.strftime() method to add year_month column\n",
    "directing_df['year_month'] = pd.to_datetime(directing_df['release_date']).dt.strftime('%Y-%m')\n",
    "\n",
    "directing_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movie_id                   int64\n",
       "release_date      datetime64[ns]\n",
       "percent_female           float64\n",
       "year_month        datetime64[ns]\n",
       "dtype: object"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert year_month column to datetime format\n",
    "directing_df['year_month'] = pd.to_datetime(directing_df['year_month'])\n",
    "\n",
    "directing_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movie_id                   int64\n",
       "release_date      datetime64[ns]\n",
       "percent_female           float64\n",
       "year_month                 int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert year_month to ordinal for ML\n",
    "directing_df['year_month']=directing_df['year_month'].map(dt.datetime.toordinal)\n",
    "\n",
    "directing_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year_month\n",
       "711858    0.000000\n",
       "711889    0.000000\n",
       "711917    0.000000\n",
       "711948    0.000000\n",
       "711978    0.041667\n",
       "            ...   \n",
       "736511    0.131579\n",
       "736542    0.000000\n",
       "736573    0.000000\n",
       "736603    0.000000\n",
       "736664    0.000000\n",
       "Name: percent_female, Length: 815, dtype: float64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "directing_count = directing_df.groupby(['year_month'])['percent_female'].mean()\n",
    "\n",
    "directing_count"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DONT RERUN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year_month</th>\n",
       "      <th>percent_female</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>711858</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>711889</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>711917</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>711948</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>711978</td>\n",
       "      <td>0.041667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>810</th>\n",
       "      <td>736511</td>\n",
       "      <td>0.131579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>811</th>\n",
       "      <td>736542</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>812</th>\n",
       "      <td>736573</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>813</th>\n",
       "      <td>736603</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>814</th>\n",
       "      <td>736664</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>815 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     year_month  percent_female\n",
       "0        711858        0.000000\n",
       "1        711889        0.000000\n",
       "2        711917        0.000000\n",
       "3        711948        0.000000\n",
       "4        711978        0.041667\n",
       "..          ...             ...\n",
       "810      736511        0.131579\n",
       "811      736542        0.000000\n",
       "812      736573        0.000000\n",
       "813      736603        0.000000\n",
       "814      736664        0.000000\n",
       "\n",
       "[815 rows x 2 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Change from series to df\n",
    "directing = directing_count.to_frame()\n",
    "\n",
    "# Reset index  DO NOT RERUN\n",
    "# Reset index  DO NOT RERUN\n",
    "directing  = directing.rename_axis('year_month').reset_index()\n",
    "\n",
    "# Review df\n",
    "directing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Review data distribution before applying ML model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Preview data\n",
    "plt.scatter(directing.year_month, directing.percent_female)\n",
    "plt.xlabel('Release Date')\n",
    "plt.ylabel('Percentage of Female Directing Staff')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[711858],\n",
       "       [711889],\n",
       "       [711917],\n",
       "       [711948],\n",
       "       [711978],\n",
       "       [712009],\n",
       "       [712039],\n",
       "       [712070],\n",
       "       [712101],\n",
       "       [712131],\n",
       "       [712162],\n",
       "       [712192],\n",
       "       [712223],\n",
       "       [712254],\n",
       "       [712282],\n",
       "       [712313],\n",
       "       [712343],\n",
       "       [712374],\n",
       "       [712404],\n",
       "       [712435],\n",
       "       [712466],\n",
       "       [712496],\n",
       "       [712527],\n",
       "       [712557],\n",
       "       [712588],\n",
       "       [712619],\n",
       "       [712648],\n",
       "       [712679],\n",
       "       [712709],\n",
       "       [712740],\n",
       "       [712770],\n",
       "       [712801],\n",
       "       [712832],\n",
       "       [712862],\n",
       "       [712893],\n",
       "       [712923],\n",
       "       [712954],\n",
       "       [712985],\n",
       "       [713013],\n",
       "       [713044],\n",
       "       [713074],\n",
       "       [713105],\n",
       "       [713135],\n",
       "       [713166],\n",
       "       [713197],\n",
       "       [713227],\n",
       "       [713258],\n",
       "       [713288],\n",
       "       [713319],\n",
       "       [713350],\n",
       "       [713378],\n",
       "       [713409],\n",
       "       [713439],\n",
       "       [713470],\n",
       "       [713500],\n",
       "       [713531],\n",
       "       [713562],\n",
       "       [713592],\n",
       "       [713623],\n",
       "       [713653],\n",
       "       [713684],\n",
       "       [713715],\n",
       "       [713743],\n",
       "       [713774],\n",
       "       [713804],\n",
       "       [713835],\n",
       "       [713865],\n",
       "       [713896],\n",
       "       [713927],\n",
       "       [713957],\n",
       "       [713988],\n",
       "       [714018],\n",
       "       [714049],\n",
       "       [714080],\n",
       "       [714109],\n",
       "       [714140],\n",
       "       [714170],\n",
       "       [714201],\n",
       "       [714231],\n",
       "       [714262],\n",
       "       [714293],\n",
       "       [714323],\n",
       "       [714354],\n",
       "       [714384],\n",
       "       [714415],\n",
       "       [714446],\n",
       "       [714474],\n",
       "       [714505],\n",
       "       [714535],\n",
       "       [714566],\n",
       "       [714596],\n",
       "       [714627],\n",
       "       [714658],\n",
       "       [714688],\n",
       "       [714719],\n",
       "       [714749],\n",
       "       [714780],\n",
       "       [714811],\n",
       "       [714839],\n",
       "       [714870],\n",
       "       [714900],\n",
       "       [714931],\n",
       "       [714961],\n",
       "       [714992],\n",
       "       [715023],\n",
       "       [715053],\n",
       "       [715084],\n",
       "       [715114],\n",
       "       [715145],\n",
       "       [715176],\n",
       "       [715204],\n",
       "       [715235],\n",
       "       [715265],\n",
       "       [715296],\n",
       "       [715326],\n",
       "       [715357],\n",
       "       [715388],\n",
       "       [715418],\n",
       "       [715449],\n",
       "       [715479],\n",
       "       [715510],\n",
       "       [715541],\n",
       "       [715570],\n",
       "       [715601],\n",
       "       [715631],\n",
       "       [715662],\n",
       "       [715692],\n",
       "       [715723],\n",
       "       [715754],\n",
       "       [715784],\n",
       "       [715815],\n",
       "       [715845],\n",
       "       [715876],\n",
       "       [715907],\n",
       "       [715935],\n",
       "       [715966],\n",
       "       [715996],\n",
       "       [716027],\n",
       "       [716057],\n",
       "       [716088],\n",
       "       [716119],\n",
       "       [716149],\n",
       "       [716180],\n",
       "       [716210],\n",
       "       [716241],\n",
       "       [716272],\n",
       "       [716300],\n",
       "       [716331],\n",
       "       [716361],\n",
       "       [716392],\n",
       "       [716422],\n",
       "       [716453],\n",
       "       [716484],\n",
       "       [716514],\n",
       "       [716545],\n",
       "       [716575],\n",
       "       [716606],\n",
       "       [716637],\n",
       "       [716665],\n",
       "       [716696],\n",
       "       [716726],\n",
       "       [716757],\n",
       "       [716787],\n",
       "       [716818],\n",
       "       [716849],\n",
       "       [716879],\n",
       "       [716910],\n",
       "       [716940],\n",
       "       [716971],\n",
       "       [717002],\n",
       "       [717031],\n",
       "       [717062],\n",
       "       [717092],\n",
       "       [717123],\n",
       "       [717153],\n",
       "       [717184],\n",
       "       [717215],\n",
       "       [717245],\n",
       "       [717276],\n",
       "       [717306],\n",
       "       [717337],\n",
       "       [717368],\n",
       "       [717396],\n",
       "       [717427],\n",
       "       [717457],\n",
       "       [717488],\n",
       "       [717518],\n",
       "       [717549],\n",
       "       [717580],\n",
       "       [717610],\n",
       "       [717641],\n",
       "       [717671],\n",
       "       [717702],\n",
       "       [717733],\n",
       "       [717761],\n",
       "       [717792],\n",
       "       [717822],\n",
       "       [717853],\n",
       "       [717883],\n",
       "       [717914],\n",
       "       [717945],\n",
       "       [717975],\n",
       "       [718006],\n",
       "       [718036],\n",
       "       [718067],\n",
       "       [718098],\n",
       "       [718126],\n",
       "       [718157],\n",
       "       [718187],\n",
       "       [718218],\n",
       "       [718248],\n",
       "       [718279],\n",
       "       [718310],\n",
       "       [718340],\n",
       "       [718371],\n",
       "       [718401],\n",
       "       [718432],\n",
       "       [718463],\n",
       "       [718492],\n",
       "       [718523],\n",
       "       [718553],\n",
       "       [718584],\n",
       "       [718614],\n",
       "       [718645],\n",
       "       [718676],\n",
       "       [718706],\n",
       "       [718737],\n",
       "       [718767],\n",
       "       [718798],\n",
       "       [718829],\n",
       "       [718857],\n",
       "       [718888],\n",
       "       [718918],\n",
       "       [718949],\n",
       "       [718979],\n",
       "       [719010],\n",
       "       [719041],\n",
       "       [719071],\n",
       "       [719102],\n",
       "       [719132],\n",
       "       [719163],\n",
       "       [719194],\n",
       "       [719222],\n",
       "       [719253],\n",
       "       [719283],\n",
       "       [719314],\n",
       "       [719344],\n",
       "       [719375],\n",
       "       [719406],\n",
       "       [719436],\n",
       "       [719467],\n",
       "       [719497],\n",
       "       [719528],\n",
       "       [719559],\n",
       "       [719587],\n",
       "       [719618],\n",
       "       [719648],\n",
       "       [719679],\n",
       "       [719709],\n",
       "       [719740],\n",
       "       [719771],\n",
       "       [719801],\n",
       "       [719832],\n",
       "       [719862],\n",
       "       [719893],\n",
       "       [719924],\n",
       "       [719953],\n",
       "       [719984],\n",
       "       [720014],\n",
       "       [720045],\n",
       "       [720075],\n",
       "       [720106],\n",
       "       [720137],\n",
       "       [720167],\n",
       "       [720198],\n",
       "       [720228],\n",
       "       [720259],\n",
       "       [720290],\n",
       "       [720318],\n",
       "       [720349],\n",
       "       [720379],\n",
       "       [720410],\n",
       "       [720440],\n",
       "       [720471],\n",
       "       [720502],\n",
       "       [720532],\n",
       "       [720563],\n",
       "       [720593],\n",
       "       [720624],\n",
       "       [720655],\n",
       "       [720683],\n",
       "       [720714],\n",
       "       [720744],\n",
       "       [720775],\n",
       "       [720805],\n",
       "       [720836],\n",
       "       [720867],\n",
       "       [720897],\n",
       "       [720928],\n",
       "       [720958],\n",
       "       [720989],\n",
       "       [721020],\n",
       "       [721048],\n",
       "       [721079],\n",
       "       [721109],\n",
       "       [721140],\n",
       "       [721170],\n",
       "       [721201],\n",
       "       [721232],\n",
       "       [721262],\n",
       "       [721293],\n",
       "       [721323],\n",
       "       [721354],\n",
       "       [721385],\n",
       "       [721414],\n",
       "       [721445],\n",
       "       [721475],\n",
       "       [721506],\n",
       "       [721536],\n",
       "       [721567],\n",
       "       [721598],\n",
       "       [721628],\n",
       "       [721659],\n",
       "       [721689],\n",
       "       [721720],\n",
       "       [721751],\n",
       "       [721779],\n",
       "       [721810],\n",
       "       [721840],\n",
       "       [721871],\n",
       "       [721901],\n",
       "       [721932],\n",
       "       [721963],\n",
       "       [721993],\n",
       "       [722024],\n",
       "       [722054],\n",
       "       [722085],\n",
       "       [722116],\n",
       "       [722144],\n",
       "       [722175],\n",
       "       [722205],\n",
       "       [722236],\n",
       "       [722266],\n",
       "       [722297],\n",
       "       [722328],\n",
       "       [722358],\n",
       "       [722389],\n",
       "       [722419],\n",
       "       [722450],\n",
       "       [722481],\n",
       "       [722509],\n",
       "       [722540],\n",
       "       [722570],\n",
       "       [722601],\n",
       "       [722631],\n",
       "       [722662],\n",
       "       [722693],\n",
       "       [722723],\n",
       "       [722754],\n",
       "       [722784],\n",
       "       [722815],\n",
       "       [722846],\n",
       "       [722875],\n",
       "       [722906],\n",
       "       [722936],\n",
       "       [722967],\n",
       "       [722997],\n",
       "       [723028],\n",
       "       [723059],\n",
       "       [723089],\n",
       "       [723120],\n",
       "       [723150],\n",
       "       [723181],\n",
       "       [723212],\n",
       "       [723240],\n",
       "       [723271],\n",
       "       [723301],\n",
       "       [723332],\n",
       "       [723362],\n",
       "       [723393],\n",
       "       [723424],\n",
       "       [723454],\n",
       "       [723485],\n",
       "       [723515],\n",
       "       [723546],\n",
       "       [723577],\n",
       "       [723605],\n",
       "       [723636],\n",
       "       [723666],\n",
       "       [723697],\n",
       "       [723727],\n",
       "       [723758],\n",
       "       [723789],\n",
       "       [723819],\n",
       "       [723850],\n",
       "       [723880],\n",
       "       [723911],\n",
       "       [723942],\n",
       "       [723970],\n",
       "       [724001],\n",
       "       [724031],\n",
       "       [724062],\n",
       "       [724092],\n",
       "       [724123],\n",
       "       [724154],\n",
       "       [724184],\n",
       "       [724215],\n",
       "       [724245],\n",
       "       [724276],\n",
       "       [724307],\n",
       "       [724336],\n",
       "       [724367],\n",
       "       [724397],\n",
       "       [724428],\n",
       "       [724458],\n",
       "       [724489],\n",
       "       [724520],\n",
       "       [724550],\n",
       "       [724581],\n",
       "       [724611],\n",
       "       [724642],\n",
       "       [724673],\n",
       "       [724701],\n",
       "       [724732],\n",
       "       [724762],\n",
       "       [724793],\n",
       "       [724823],\n",
       "       [724854],\n",
       "       [724885],\n",
       "       [724915],\n",
       "       [724946],\n",
       "       [724976],\n",
       "       [725007],\n",
       "       [725038],\n",
       "       [725066],\n",
       "       [725097],\n",
       "       [725127],\n",
       "       [725158],\n",
       "       [725188],\n",
       "       [725219],\n",
       "       [725250],\n",
       "       [725280],\n",
       "       [725311],\n",
       "       [725341],\n",
       "       [725372],\n",
       "       [725403],\n",
       "       [725431],\n",
       "       [725462],\n",
       "       [725492],\n",
       "       [725523],\n",
       "       [725553],\n",
       "       [725584],\n",
       "       [725615],\n",
       "       [725645],\n",
       "       [725676],\n",
       "       [725706],\n",
       "       [725737],\n",
       "       [725768],\n",
       "       [725797],\n",
       "       [725828],\n",
       "       [725858],\n",
       "       [725889],\n",
       "       [725919],\n",
       "       [725950],\n",
       "       [725981],\n",
       "       [726011],\n",
       "       [726042],\n",
       "       [726072],\n",
       "       [726103],\n",
       "       [726134],\n",
       "       [726162],\n",
       "       [726193],\n",
       "       [726223],\n",
       "       [726254],\n",
       "       [726284],\n",
       "       [726315],\n",
       "       [726346],\n",
       "       [726376],\n",
       "       [726407],\n",
       "       [726437],\n",
       "       [726468],\n",
       "       [726499],\n",
       "       [726527],\n",
       "       [726558],\n",
       "       [726588],\n",
       "       [726619],\n",
       "       [726649],\n",
       "       [726680],\n",
       "       [726711],\n",
       "       [726741],\n",
       "       [726772],\n",
       "       [726802],\n",
       "       [726833],\n",
       "       [726864],\n",
       "       [726892],\n",
       "       [726923],\n",
       "       [726953],\n",
       "       [726984],\n",
       "       [727014],\n",
       "       [727045],\n",
       "       [727076],\n",
       "       [727106],\n",
       "       [727137],\n",
       "       [727167],\n",
       "       [727198],\n",
       "       [727229],\n",
       "       [727258],\n",
       "       [727289],\n",
       "       [727319],\n",
       "       [727350],\n",
       "       [727380],\n",
       "       [727411],\n",
       "       [727442],\n",
       "       [727472],\n",
       "       [727503],\n",
       "       [727533],\n",
       "       [727564],\n",
       "       [727595],\n",
       "       [727623],\n",
       "       [727654],\n",
       "       [727684],\n",
       "       [727715],\n",
       "       [727745],\n",
       "       [727776],\n",
       "       [727807],\n",
       "       [727837],\n",
       "       [727868],\n",
       "       [727898],\n",
       "       [727929],\n",
       "       [727960],\n",
       "       [727988],\n",
       "       [728019],\n",
       "       [728049],\n",
       "       [728080],\n",
       "       [728110],\n",
       "       [728141],\n",
       "       [728172],\n",
       "       [728202],\n",
       "       [728233],\n",
       "       [728263],\n",
       "       [728294],\n",
       "       [728325],\n",
       "       [728353],\n",
       "       [728384],\n",
       "       [728414],\n",
       "       [728445],\n",
       "       [728475],\n",
       "       [728506],\n",
       "       [728537],\n",
       "       [728567],\n",
       "       [728598],\n",
       "       [728628],\n",
       "       [728659],\n",
       "       [728690],\n",
       "       [728719],\n",
       "       [728750],\n",
       "       [728780],\n",
       "       [728811],\n",
       "       [728841],\n",
       "       [728872],\n",
       "       [728903],\n",
       "       [728933],\n",
       "       [728964],\n",
       "       [728994],\n",
       "       [729025],\n",
       "       [729056],\n",
       "       [729084],\n",
       "       [729115],\n",
       "       [729145],\n",
       "       [729176],\n",
       "       [729206],\n",
       "       [729237],\n",
       "       [729268],\n",
       "       [729298],\n",
       "       [729329],\n",
       "       [729359],\n",
       "       [729390],\n",
       "       [729421],\n",
       "       [729449],\n",
       "       [729480],\n",
       "       [729510],\n",
       "       [729541],\n",
       "       [729571],\n",
       "       [729602],\n",
       "       [729633],\n",
       "       [729663],\n",
       "       [729694],\n",
       "       [729724],\n",
       "       [729755],\n",
       "       [729786],\n",
       "       [729814],\n",
       "       [729845],\n",
       "       [729875],\n",
       "       [729906],\n",
       "       [729936],\n",
       "       [729967],\n",
       "       [729998],\n",
       "       [730028],\n",
       "       [730059],\n",
       "       [730089],\n",
       "       [730120],\n",
       "       [730151],\n",
       "       [730180],\n",
       "       [730211],\n",
       "       [730241],\n",
       "       [730272],\n",
       "       [730302],\n",
       "       [730333],\n",
       "       [730364],\n",
       "       [730394],\n",
       "       [730425],\n",
       "       [730455],\n",
       "       [730486],\n",
       "       [730517],\n",
       "       [730545],\n",
       "       [730576],\n",
       "       [730606],\n",
       "       [730637],\n",
       "       [730667],\n",
       "       [730698],\n",
       "       [730729],\n",
       "       [730759],\n",
       "       [730790],\n",
       "       [730820],\n",
       "       [730851],\n",
       "       [730882],\n",
       "       [730910],\n",
       "       [730941],\n",
       "       [730971],\n",
       "       [731002],\n",
       "       [731032],\n",
       "       [731063],\n",
       "       [731094],\n",
       "       [731124],\n",
       "       [731155],\n",
       "       [731185],\n",
       "       [731216],\n",
       "       [731247],\n",
       "       [731275],\n",
       "       [731306],\n",
       "       [731336],\n",
       "       [731367],\n",
       "       [731397],\n",
       "       [731428],\n",
       "       [731459],\n",
       "       [731489],\n",
       "       [731520],\n",
       "       [731550],\n",
       "       [731581],\n",
       "       [731612],\n",
       "       [731641],\n",
       "       [731672],\n",
       "       [731702],\n",
       "       [731733],\n",
       "       [731763],\n",
       "       [731794],\n",
       "       [731825],\n",
       "       [731855],\n",
       "       [731886],\n",
       "       [731916],\n",
       "       [731947],\n",
       "       [731978],\n",
       "       [732006],\n",
       "       [732037],\n",
       "       [732067],\n",
       "       [732098],\n",
       "       [732128],\n",
       "       [732159],\n",
       "       [732190],\n",
       "       [732220],\n",
       "       [732251],\n",
       "       [732281],\n",
       "       [732312],\n",
       "       [732343],\n",
       "       [732371],\n",
       "       [732402],\n",
       "       [732432],\n",
       "       [732463],\n",
       "       [732493],\n",
       "       [732524],\n",
       "       [732555],\n",
       "       [732585],\n",
       "       [732616],\n",
       "       [732646],\n",
       "       [732677],\n",
       "       [732708],\n",
       "       [732736],\n",
       "       [732767],\n",
       "       [732797],\n",
       "       [732828],\n",
       "       [732858],\n",
       "       [732889],\n",
       "       [732920],\n",
       "       [732950],\n",
       "       [732981],\n",
       "       [733011],\n",
       "       [733042],\n",
       "       [733073],\n",
       "       [733102],\n",
       "       [733133],\n",
       "       [733163],\n",
       "       [733194],\n",
       "       [733224],\n",
       "       [733255],\n",
       "       [733286],\n",
       "       [733316],\n",
       "       [733347],\n",
       "       [733377],\n",
       "       [733408],\n",
       "       [733439],\n",
       "       [733467],\n",
       "       [733498],\n",
       "       [733528],\n",
       "       [733559],\n",
       "       [733589],\n",
       "       [733620],\n",
       "       [733651],\n",
       "       [733681],\n",
       "       [733712],\n",
       "       [733742],\n",
       "       [733773],\n",
       "       [733804],\n",
       "       [733832],\n",
       "       [733863],\n",
       "       [733893],\n",
       "       [733924],\n",
       "       [733954],\n",
       "       [733985],\n",
       "       [734016],\n",
       "       [734046],\n",
       "       [734077],\n",
       "       [734107],\n",
       "       [734138],\n",
       "       [734169],\n",
       "       [734197],\n",
       "       [734228],\n",
       "       [734258],\n",
       "       [734289],\n",
       "       [734319],\n",
       "       [734350],\n",
       "       [734381],\n",
       "       [734411],\n",
       "       [734442],\n",
       "       [734472],\n",
       "       [734503],\n",
       "       [734534],\n",
       "       [734563],\n",
       "       [734594],\n",
       "       [734624],\n",
       "       [734655],\n",
       "       [734685],\n",
       "       [734716],\n",
       "       [734747],\n",
       "       [734777],\n",
       "       [734808],\n",
       "       [734838],\n",
       "       [734869],\n",
       "       [734900],\n",
       "       [734928],\n",
       "       [734959],\n",
       "       [734989],\n",
       "       [735020],\n",
       "       [735050],\n",
       "       [735081],\n",
       "       [735112],\n",
       "       [735142],\n",
       "       [735173],\n",
       "       [735203],\n",
       "       [735234],\n",
       "       [735265],\n",
       "       [735293],\n",
       "       [735324],\n",
       "       [735354],\n",
       "       [735385],\n",
       "       [735415],\n",
       "       [735446],\n",
       "       [735477],\n",
       "       [735507],\n",
       "       [735538],\n",
       "       [735568],\n",
       "       [735599],\n",
       "       [735630],\n",
       "       [735658],\n",
       "       [735689],\n",
       "       [735719],\n",
       "       [735750],\n",
       "       [735780],\n",
       "       [735811],\n",
       "       [735842],\n",
       "       [735872],\n",
       "       [735903],\n",
       "       [735933],\n",
       "       [735964],\n",
       "       [735995],\n",
       "       [736024],\n",
       "       [736055],\n",
       "       [736085],\n",
       "       [736116],\n",
       "       [736146],\n",
       "       [736177],\n",
       "       [736208],\n",
       "       [736238],\n",
       "       [736269],\n",
       "       [736299],\n",
       "       [736330],\n",
       "       [736361],\n",
       "       [736389],\n",
       "       [736420],\n",
       "       [736450],\n",
       "       [736481],\n",
       "       [736511],\n",
       "       [736542],\n",
       "       [736573],\n",
       "       [736603],\n",
       "       [736664]], dtype=int64)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  x axis \n",
    "Xd = directing.year_month.values.reshape(-1, 1)\n",
    "\n",
    "Xd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0.000000\n",
       "1      0.000000\n",
       "2      0.000000\n",
       "3      0.000000\n",
       "4      0.041667\n",
       "         ...   \n",
       "810    0.131579\n",
       "811    0.000000\n",
       "812    0.000000\n",
       "813    0.000000\n",
       "814    0.000000\n",
       "Name: percent_female, Length: 815, dtype: float64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yd = directing.percent_female\n",
    "\n",
    "yd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_dir = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_dir.fit(Xd, yd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(815,)\n",
      "815\n"
     ]
    }
   ],
   "source": [
    "yd_pred = model_dir.predict(Xd)\n",
    "print(yd_pred.shape)\n",
    "print(len(yd_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1950-01\n",
       "1      1950-02\n",
       "2      1950-03\n",
       "3      1950-04\n",
       "4      1950-05\n",
       "        ...   \n",
       "810    2017-07\n",
       "811    2017-08\n",
       "812    2017-09\n",
       "813    2017-10\n",
       "814    2017-12\n",
       "Length: 815, dtype: object"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xd_transform = pd.Series(list(Xd))\n",
    "Xd_transform = Xd_transform.apply(dt.datetime.fromordinal)\n",
    "Xd_transform = pd.to_datetime(Xd_transform).dt.strftime('%Y-%m')\n",
    "\n",
    "Xd_transform"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Direction Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 900x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9,9))\n",
    "\n",
    "plt.scatter(Xd_transform, yd)\n",
    "plt.plot(Xd_transform, yd_pred, color='red')\n",
    "plt.xlabel('Release Date')\n",
    "plt.ylabel('Percentage of Female Staff (Directing)')\n",
    "\n",
    "plt.xticks(np.arange(0, len(Xd_transform)+1, 60),rotation=45)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Direction linear regression equation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Weight coefficients:  [6.37528608e-06]\n",
      "y-axis intercept:  -4.55287217983898\n"
     ]
    }
   ],
   "source": [
    "print('Weight coefficients: ', model_dir.coef_)\n",
    "print('y-axis intercept: ', model_dir.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year_month</th>\n",
       "      <th>percent_female</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>year_month</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.721191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>percent_female</th>\n",
       "      <td>0.721191</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                year_month  percent_female\n",
       "year_month        1.000000        0.721191\n",
       "percent_female    0.721191        1.000000"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "directing.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Correlation of 0.70 shows moderate positive correlation between time and female participation in the directing department"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When would we have a 50% of female participation in the Directing department according to the linear regression model?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[792571.83375597]\n"
     ]
    }
   ],
   "source": [
    "directing_fiftypercent = calc_year_month(model_dir.coef_,model_dir.intercept_, 0.5)\n",
    "\n",
    "print(directing_fiftypercent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2170-12'"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert ordinal number to readable format\n",
    "# fromordinal needs an int instead of a float\n",
    "# use following solution but convert directing_fiftypercent to int inside function\n",
    "# https://stackoverflow.com/questions/16542074/what-is-the-inverse-of-date-toordinal-in-python\n",
    "directing_fifty_conversion = datetime.fromordinal(int(directing_fiftypercent)).strftime('%Y-%m')\n",
    "\n",
    "directing_fifty_conversion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Directing accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions_dir = model_dir.predict(Xd)\n",
    "# error delete prediction vs actual data\n",
    "error_dir = predictions_dir - yd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Predicted</th>\n",
       "      <th>Actual</th>\n",
       "      <th>Error</th>\n",
       "      <th>MSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.014574</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.014574</td>\n",
       "      <td>0.000212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.014376</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.014376</td>\n",
       "      <td>0.000207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.014198</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.014198</td>\n",
       "      <td>0.000202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.014000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.014000</td>\n",
       "      <td>0.000196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.013809</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>-0.055475</td>\n",
       "      <td>0.003078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>810</th>\n",
       "      <td>0.142596</td>\n",
       "      <td>0.131579</td>\n",
       "      <td>0.011017</td>\n",
       "      <td>0.000121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>811</th>\n",
       "      <td>0.142794</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.142794</td>\n",
       "      <td>0.020390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>812</th>\n",
       "      <td>0.142991</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.142991</td>\n",
       "      <td>0.020447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>813</th>\n",
       "      <td>0.143183</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.143183</td>\n",
       "      <td>0.020501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>814</th>\n",
       "      <td>0.143572</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.143572</td>\n",
       "      <td>0.020613</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>815 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Predicted    Actual     Error       MSE\n",
       "0    -0.014574  0.000000 -0.014574  0.000212\n",
       "1    -0.014376  0.000000 -0.014376  0.000207\n",
       "2    -0.014198  0.000000 -0.014198  0.000202\n",
       "3    -0.014000  0.000000 -0.014000  0.000196\n",
       "4    -0.013809  0.041667 -0.055475  0.003078\n",
       "..         ...       ...       ...       ...\n",
       "810   0.142596  0.131579  0.011017  0.000121\n",
       "811   0.142794  0.000000  0.142794  0.020390\n",
       "812   0.142991  0.000000  0.142991  0.020447\n",
       "813   0.143183  0.000000  0.143183  0.020501\n",
       "814   0.143572  0.000000  0.143572  0.020613\n",
       "\n",
       "[815 rows x 4 columns]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "directing_error_df = pd.DataFrame({\"Predicted\": predictions_dir, \"Actual\": yd, \"Error\": error_dir, \"MSE\":error_dir**2})[[\"Predicted\", \"Actual\", \"Error\", \"MSE\"]]\n",
    "\n",
    "directing_error_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Predicted</th>\n",
       "      <th>Actual</th>\n",
       "      <th>Error</th>\n",
       "      <th>MSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>815.000000</td>\n",
       "      <td>815.000000</td>\n",
       "      <td>8.150000e+02</td>\n",
       "      <td>8.150000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.064399</td>\n",
       "      <td>0.064399</td>\n",
       "      <td>5.308637e-16</td>\n",
       "      <td>1.923067e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.045682</td>\n",
       "      <td>0.063343</td>\n",
       "      <td>4.387971e-02</td>\n",
       "      <td>3.963541e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.014574</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-2.333601e-01</td>\n",
       "      <td>3.378821e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.024912</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-2.372093e-02</td>\n",
       "      <td>1.136774e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.064397</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>3.468279e-03</td>\n",
       "      <td>7.305542e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.103892</td>\n",
       "      <td>0.108696</td>\n",
       "      <td>2.879121e-02</td>\n",
       "      <td>2.182776e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.143572</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>1.435716e-01</td>\n",
       "      <td>5.445696e-02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Predicted      Actual         Error           MSE\n",
       "count  815.000000  815.000000  8.150000e+02  8.150000e+02\n",
       "mean     0.064399    0.064399  5.308637e-16  1.923067e-03\n",
       "std      0.045682    0.063343  4.387971e-02  3.963541e-03\n",
       "min     -0.014574    0.000000 -2.333601e-01  3.378821e-10\n",
       "25%      0.024912    0.000000 -2.372093e-02  1.136774e-04\n",
       "50%      0.064397    0.055556  3.468279e-03  7.305542e-04\n",
       "75%      0.103892    0.108696  2.879121e-02  2.182776e-03\n",
       "max      0.143572    0.375000  1.435716e-01  5.445696e-02"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "directing_error_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Residuals graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Residuals\n",
    "plt.scatter(predictions_dir, error_dir)\n",
    "plt.hlines(y=0, xmin=predictions_dir.min(), xmax=predictions_dir.max(), colors=\"red\")\n",
    "plt.title(label=\"Residuals plot for Direction department\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.13"
  },
  "vscode": {
   "interpreter": {
    "hash": "52d335269d19600ab70dcb584525db1fd411490ad9ca2609c3073fb12f84bf33"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}