IMDB_Cluster_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PCA & K-Means Cluster Analysis of Personal IMDB Ratings\n",
    "### Objectives:\n",
    "1. Attempting to Find patterns in <b>My IMDb movie Ratings </b>over a period of 5 Years.<b> Find insights about preferences etc., Important Features </b>Using (I) EDA (II) K-Means Clustering.\n",
    "2. Find <b>Interesting Clusters of Movies Using K-Means Clustering</b>.\n",
    "3. Use <b>Principal Component Analysis </b>to improve Clustering output.\n",
    "4. Gathering Insights on preferences by performing <b>hypothesis testing on these clusters</b> with respect to mean Ratings.."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Insights :\n",
    "<p> <b>Statisticaly Significant Clusters </b>indicate that the author:\n",
    "    1. Prefers - Prestige/lessser known Drama, Romantic/Comedic Dramas.\n",
    "    2. Dislikes - Newer Action-Adventure, Sci-Fi , Superhero movies, Hindi Comedies.\n",
    "It was also found that the IMDb rating, Popularity of movies were important positive features, whereas the year of release of a movie had an inverse relationship on the rating on the movie.<br>\n",
    "<b>Clusters are also formed of films by directors that the author</b> seems to prefer (high mean difference to the overall mean of ratings), These include films by Directors Satyajit Ray, Brad bird, Andrew Stanton, Dean DeBlois, Edgar Wright, Steven Spielberg, Wes Anderson etc.<br>\n",
    "<b>Clusters of Directors that make longer than average movies</b> were also formed but were not found to have a relevant effect on the preference of the author. The clusters with the most positive deviation in popularity were of films by David Fincher, Christopher Nolan."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from pathlib import Path\n",
    "import warnings\n",
    "\n",
    "pd.set_option('display.max_rows', 1000)\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = str(Path.cwd()) + '/Data/imdb_ratings_april_2020.csv'\n",
    "\n",
    "data = pd.read_csv(path, engine='python', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filter Dataframe\n",
    "\n",
    "film_filt = (data['Title Type'] == 'movie')\n",
    "data_film = data.loc[(film_filt)]\n",
    "# Keeping Backup Dataframe\n",
    "data_film_orig = data_film"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Exploratory Analysis & Visualizations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Univariate "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### BoxPlots for Quantitative Vars.\n",
    "Insights:\n",
    "1. Most of the Ratings lie between the rating 7 and 8, this is slightly lower compared to the IMDb users, But this could be misleading as Discrete vs Continuous Comaprison.\n",
    "2. The author generally likes watching Longer Movies.\n",
    "3. Although most movies watched are newer, their is a long tail towards older movies, author doesn't mind the occassional older movies.\n",
    "4. Majority of the movies watched are on the lower end of Popularity. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 496,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_box_num_uni(df):\n",
    "    \"\"\" Plots Boxplots for all Numerical Columns in Dataframe.\n",
    "    \"\"\"\n",
    "    fig = plt.figure()\n",
    "    fig.set_figheight(30)\n",
    "    fig.set_figwidth(20)\n",
    "    for i, item in enumerate(df.select_dtypes([np.number]).columns.to_list()):\n",
    "        ax = fig.add_subplot(len(df.select_dtypes([np.number]).columns.to_list()), 3, i+1)\n",
    "        sns.boxplot(x = str(item), data = df)\n",
    "        ax.set_title(item)\n",
    "\n",
    "    fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 497,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x2160 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_box_num_uni(data_film)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Division of Ratings accross Genres"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_film[['Genre1','Genre2','Genre3','Genre4','Genre5','Genre6','Genre7']] = data_film.Genres.str.split(',', expand=True)\n",
    "data_film.drop(['Genre2','Genre3', 'Genre4','Genre5','Genre6','Genre7'], axis= 1, inplace = True)\n",
    "# Loss of info by dropping secondary, tertiary genres but will result in duplication, is complex."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loss of info, Lee ukreich, ethan and joel coen etc are big loosers. Complex fix later.\n",
    "data_film[['Director1','Director2','Director3', 'Director4']] = data_film.Directors.str.split(',', expand=True)\n",
    "data_film.drop(['Director2','Director3', 'Director4'], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,8))\n",
    "ax = sns.countplot(x = 'Genre1', data = data_film, hue=\"Your Rating\", palette=\"pastel\")\n",
    "ax.legend(loc='upper right', title = 'Your Rating')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Most Watched Directors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (16,6))\n",
    "data_film.Director1.value_counts()[:20].plot(kind = 'bar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bivariate Analysis With Respect to 'Your Rating'\n",
    "1. I generally follow the same trends as the IMDb ratings. The movies I've rated the highest tend to be highly rated movies in general. There is a large percentage of movies I've rated lower that the IMDb public likes.\n",
    "2. The lowest rated movies tend to have longer run times compared to the average movies. The highest rated movies tend to be longer too.\n",
    "3. Most higher rated movies are more spread out when it comes to year of release. The violin plot shows this better.\n",
    "4. There is high spread in the popularity of the movies rated higher. Will check relationship using Linear Regression later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_box_num_biv(df, categ):\n",
    "    \"\"\" Plots Boxplots for all Numerical Columns in Dataframe with respect to a target column.\n",
    "    \"\"\"\n",
    "    fig = plt.figure()\n",
    "    fig.set_figheight(30)\n",
    "    fig.set_figwidth(20)\n",
    "    for i, item in enumerate(df.select_dtypes([np.number]).columns.to_list()):\n",
    "        ax = fig.add_subplot(len(df.select_dtypes([np.number]).columns.to_list()), 2, i+1)\n",
    "        sns.boxplot(x = categ, y = str(item), data = df)\n",
    "        ax.set_title(item)\n",
    "\n",
    "    fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x2160 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_box_num_biv(data_film, 'Your Rating')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking Relationships Between Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking Pearsons Correlation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "sns.heatmap(data = data_film.corr(), annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear Regression Plots to check If there is a Linear Relationships between Your Rating and Other Quantitaive Vars., Comparison with IMDb Users.\n",
    "0. A strong relationship is not expected between Your Ratings (sample) and IMDb Ratings (population), this is backed up by the first plot. It was observed that the std deviation for Your rating is greater, which is to be expected from a sample of size 1. But this is not of interest as we want to know which features contribute most to the Ratings and whether there is significant difference between the author and the IMDB Population.\n",
    "1. There is a Strong Positive Linear Relationship between IMDb user Ratings and My Ratings, not significantly different ratings, makes sense as movies rated higher by the population would be good movies in geneal. There are quite a few outliers visible. More interested in these values as they could show possible pecularities in taste and important features.\n",
    "2. No Significant Relationship linear relationship between runtime and Your rating. There are high leverage points. Weaker relationship compared to Imdb Users.\n",
    "3. Appears to be weak negative relationship between Year of release and Your Rating, stronger than other IMDb users , author favors older movies but could be possible that only good older movies watched. (Check Leverage Statistics to see if this is a problem.)\n",
    "4. Appears to be a weak positive relationship between popularity (Num of votes) and Your rating. Some high leverage points. Comparing with Rating by IMDb users, this relationship appears to be extremely similar but larger confidence interval. So yeah, popular movies tend to be rated higher, again could just be a correlation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 535,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_lin_num_biv(df):\n",
    "    \"\"\" Plots Linear Regression best fit lines for all Numerical Columns in Dataframe with respect your rating column.\n",
    "    \"\"\"\n",
    "    fig = plt.figure()\n",
    "    fig.set_figheight(30)\n",
    "    fig.set_figwidth(20)\n",
    "    for i, item in enumerate(df.select_dtypes([np.number]).columns.to_list()):\n",
    "        ax = fig.add_subplot((len(df.select_dtypes([np.number]).columns.to_list())), 2, i+1)\n",
    "        sns.regplot(y = 'Your Rating', x = str(item), data = data_film, label = 'Your Ratings')\n",
    "        sns.regplot(y = 'IMDb Rating', x = str(item), data = data_film, label = 'IMDb Ratings')\n",
    "        ax.legend()\n",
    "        ax.set_ylabel('Rating')\n",
    "        ax.set_title('IMDb Rating, Your Rating - ' + item)\n",
    "    fig.savefig('/Graphs/Linear_rel.jpg')\n",
    "    fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 536,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x2160 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_lin_num_biv(data_film)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. PCA and K-Means Clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Engineering, Dropping Useless columns, One-hot Encodding Categorical Column like Genre, Director etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_film.drop(['Genres', 'Directors'], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Lets see if we can Eye-ball clusters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Rating')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "plt.plot( data_film['Num Votes'], data_film['Your Rating'].to_list(), 'r.')\n",
    "plt.xlabel('Num Votes')\n",
    "plt.ylabel('Rating')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Feature Engineering - One - Hot encode Genre, Directors and Dropping a few Columns, Convert Rated to Numerical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "data_film = pd.get_dummies(data_film, prefix='Genre', columns=['Genre1'])\n",
    "data_film = pd.get_dummies(data_film, prefix='Director', columns=['Director1'])\n",
    "data_film[['Rated_Year', 'Rated_month', 'Rated_Day']] = data_film['Date Rated'].str.split('-', expand = True)\n",
    "data_film.drop(['Rated_month', 'Rated_Day'], axis = 1, inplace = True)\n",
    "data_film.drop('Date Rated', axis = 1, inplace = True)\n",
    "data_film[['Release_Year', 'Release_month', 'Release_Day']] = data_film['Release Date'].str.split('-', expand = True)\n",
    "data_film.drop(['Release_month', 'Release_Day'], axis = 1, inplace = True)\n",
    "data_film.drop('Release Date', axis = 1, inplace = True)\n",
    "data_film.drop(['Title Type', 'URL'], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Making Copy as we'll drop Title and convert to numeric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_film_num = data_film\n",
    "data_film_num.drop(['Title'], axis = 1, inplace=True)\n",
    "data_film_num.drop(['Release_Year'], axis = 1, inplace=True)\n",
    "data_film_num['Rated_Year'] = data_film_num['Rated_Year'].astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualizing New Info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x125b96828>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,4))\n",
    "sns.violinplot(x = 'Rated_Year', y = 'Your Rating', data =data_film_num )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Curse of Dimensionality - 273 Features for 345 samples. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Principal Components Analysis to Reduce Dimensionality of Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Standardize Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "data_film_scaled = StandardScaler().fit_transform(data_film_num.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Restoring Indexes And Column Names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_film_scaled_df = pd.DataFrame(data = data_film_scaled, index = data_film_num.index, columns = data_film_num.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Lets Target that 50% Percent of the Variance is explained by the principal components - Explained by 120 Features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No of Components that explain 50% of Variance: 120\n"
     ]
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "target_variance = 0.50\n",
    "\n",
    "for i in range(len(data_film_scaled_df.columns)):\n",
    "    pca_film = PCA(n_components=i)\n",
    "    pca_components_film = pca_film.fit_transform(data_film_scaled_df)\n",
    "    #print(f'Total Explained Variance is {np.sum(pca_film.explained_variance_ratio_)}')\n",
    "    if np.sum(pca_film.explained_variance_ratio_) > target_variance:\n",
    "        #print(f'No of Components that explain 50% of Variance: {i}')\n",
    "        break\n",
    "\n",
    "print(f'No of Components that explain 50% of Variance: {i}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing important Features for First Principal Component\n",
    "No single features dominates, which is good."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>Weights</th>\n",
       "      <th>Features</th>\n",
       "      <th>abs_weights</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.458327</td>\n",
       "      <td>IMDb Rating</td>\n",
       "      <td>0.458327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.408970</td>\n",
       "      <td>Your Rating</td>\n",
       "      <td>0.408970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.346599</td>\n",
       "      <td>Num Votes</td>\n",
       "      <td>0.346599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.288688</td>\n",
       "      <td>Year</td>\n",
       "      <td>0.288688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-0.213825</td>\n",
       "      <td>Genre_Comedy</td>\n",
       "      <td>0.213825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.180817</td>\n",
       "      <td>Genre_Drama</td>\n",
       "      <td>0.180817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.150680</td>\n",
       "      <td>Runtime (mins)</td>\n",
       "      <td>0.150680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272</th>\n",
       "      <td>-0.142391</td>\n",
       "      <td>Rated_Year</td>\n",
       "      <td>0.142391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>0.127985</td>\n",
       "      <td>Director_Christopher Nolan</td>\n",
       "      <td>0.127985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>0.100570</td>\n",
       "      <td>Director_Frank Darabont</td>\n",
       "      <td>0.100570</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Weights                    Features  abs_weights\n",
       "1    0.458327                 IMDb Rating     0.458327\n",
       "0    0.408970                 Your Rating     0.408970\n",
       "4    0.346599                   Num Votes     0.346599\n",
       "3   -0.288688                        Year     0.288688\n",
       "9   -0.213825                Genre_Comedy     0.213825\n",
       "12   0.180817                 Genre_Drama     0.180817\n",
       "2    0.150680              Runtime (mins)     0.150680\n",
       "272 -0.142391                  Rated_Year     0.142391\n",
       "69   0.127985  Director_Christopher Nolan     0.127985\n",
       "97   0.100570     Director_Frank Darabont     0.100570"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first_comp = pca_film.components_[0]\n",
    "# Creating Dataframe for correspoding Columns in original Dataframe\n",
    "first_comps = pd.DataFrame(zip(first_comp, data_film_scaled_df.columns), columns = ['Weights', 'Features'])\n",
    "first_comps['abs_weights']=first_comps['Weights'].apply(lambda x: np.abs(x))\n",
    "first_comps.sort_values('abs_weights', ascending=False)[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_pca_film = list(pca_components_film)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing the High Dimensional data and Principal Components using T-SNE\n",
    "Some clusters are visible by eye in the tsne output of the pricipal components, lets see if the Clustering algorithm picks this up."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.manifold import TSNE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "embed_scale = TSNE(n_components=2).fit_transform(data_film_scaled_df)\n",
    "embed_pc = TSNE(n_components=2).fit_transform(pca_components_film)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1232a0128>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "plt.title('Using All Scaled Features')\n",
    "plt.scatter(embed_scale[:,:1],embed_scale[:,1:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x129ba22e8>"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "plt.title('Using Scaled Principal Components')\n",
    "plt.scatter(embed_pc[:,:1],embed_pc[:,1:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fitting Kmeans - Clustering Using Principal Components"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finding Optimal Number of Clusters - Using Elbow Method and SSE Score\n",
    "1. An Elbow somewhat appears at 85 clusters. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# calculate distortion for a range of number of cluster\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "distortions = []\n",
    "for i in range(1,200):\n",
    "    cluster_film = KMeans(n_clusters = i, n_init=10, max_iter= 100).fit(list_pca_film)\n",
    "    distortions.append(cluster_film.inertia_)\n",
    "    \n",
    "plt.xlabel('No. Of Clusters')\n",
    "plt.ylabel('SSE Score')\n",
    "plt.plot(range(1, 200), distortions, marker='o')\n",
    "plt.savefig('/Graphs/Clusters_SSE_plot.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster_film = KMeans(n_clusters = 85, n_init=10, max_iter= 100).fit(list_pca_film)\n",
    "pred_film = cluster_film.labels_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lets Place the cluster labels back into dataframe and check which Movies got Clustered Together."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_film_orig = data.loc[(film_filt)]\n",
    "data_film_orig['Cluster'] = pred_film\n",
    "# saving clusters.\n",
    "data_film_orig.to_csv('/Data/Clusters_from_k_means_pca.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizing and Analyzing the Clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 538,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grouped_df = data_film_orig.groupby(['Cluster'])\n",
    "embed_df = pd.DataFrame({'ax1': embed_scale[:,:1].ravel(), 'ax2': embed_scale[:,1:].ravel(), 'clusters': pred_film})\n",
    "embed_pc_df = pd.DataFrame({'ax1': embed_pc[:,:1].ravel(), 'ax2': embed_pc[:,1:].ravel(), 'clusters': pred_film})\n",
    "grouped_df_pc = embed_pc_df.groupby('clusters')\n",
    "plt.figure(figsize = (12,8))\n",
    "plt.gca().set_prop_cycle(plt.cycler('color', plt.cm.gist_ncar(np.linspace(0, 1, 84))))\n",
    "for i in range(85):\n",
    "    plt.scatter(grouped_df_pc.get_group(i).ax1.tolist(), grouped_df_pc.get_group(i).ax2.tolist(), label = i)\n",
    "plt.title('T-SNE Visualization of Clusters Obtained with K-Means and Principal Components')\n",
    "plt.savefig('/Graphs/Cluster_formed.jpg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Investigating Clusters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cluster Statistics - \n",
    "1. Checking Difference in Mean of Your Rating and IMDb Rating for clusters.\n",
    "2. Mean difference in Runtime(Length).\n",
    "3. Median Num Votes(Popularity) as data is Highly Skewed.\n",
    "4. Median Year of Release."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_film_cluster_stats = data_film_orig.groupby(['Cluster'])['Your Rating'].count()\n",
    "data_film_cluster_stats = data_film_cluster_stats.to_frame('Count')\n",
    "data_film_cluster_stats['your_imdb_Rating_Mean_Difference'] = data_film_orig.groupby(['Cluster'])['Your Rating'].mean() - data_film_orig.groupby(['Cluster'])['IMDb Rating'].mean()\n",
    "data_film_cluster_stats['RunTime_Mean_Difference'] = data_film_orig.groupby(['Cluster'])['Runtime (mins)'].mean() - data_film_orig['Runtime (mins)'].mean()\n",
    "data_film_cluster_stats['votes_Median_Difference'] = data_film_orig.groupby(['Cluster'])['Num Votes'].median() - data_film_orig['Num Votes'].median()\n",
    "data_film_cluster_stats['Year_Median_diff'] = data_film_orig.groupby(['Cluster'])['Year'].median() - data_film_orig['Year'].median()\n",
    "data_film_cluster_stats.sort_values('Count', ascending=False, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1 Sample T-Test to compare population your rating and cluster your rating."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 470,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lets perform 1 sample two-tailed t test between population your rating (data_film) and sample clust_0\n",
    "from scipy.stats import ttest_1samp\n",
    "\n",
    "def ttest_1samp_cluster_pop(cluster_number):\n",
    "    \"\"\" Testing to see if there is a significant difference between ratings given to films in cluster and \n",
    "    the population (Ratings given to all films).\n",
    "    Null (h0) - There is no difference. (So, No preference to this cluster.)\n",
    "    Alternate (h1) - There is a difference, these are rated higher/lower by the user.\n",
    "    \"\"\"\n",
    "    pop_film = data_film['Your Rating'].tolist()\n",
    "    print(f'Population Mean {np.mean(pop_film)}, degrees of freedom {len(pop_film)}')\n",
    "\n",
    "    clust_3_sample = grouped_df.get_group(cluster_number)['Your Rating'].tolist()\n",
    "    print(f'Sample mean {np.mean(clust_3_sample)}, degrees of freedom {len(clust_3_sample)}')\n",
    "\n",
    "    tstat, pvalue = ttest_1samp(clust_3_sample, np.mean(pop_film))\n",
    "    print('\\nT-Stat for hypothesis test {} and P-value {}'.format(tstat, pvalue))\n",
    "    print('T-critical for n = {}, alpha = 0.05 , look up in table'.format(len(clust_3_sample)-1))\n",
    "    if pvalue<0.05:\n",
    "        print('Statistically significant result, Reject the NUll - \\nThere is siginificant difference between ratings given to films in this group and the population.')\n",
    "    else:\n",
    "        print('Statistically insignificant result, Failled to reject the null.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_genre_cluster(genre = 'Animation'):\n",
    "    return data_film_orig.loc[data_film.Genres.str.split(',', expand=True)[0] == genre].sort_values('Cluster')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Clusters of Interest on the basis of Mean Difference in Rating between Your Rating and IMDb Ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 531,
   "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>Count</th>\n",
       "      <th>your_imdb_Rating_Mean_Difference</th>\n",
       "      <th>RunTime_Mean_Difference</th>\n",
       "      <th>votes_Median_Difference</th>\n",
       "      <th>Year_Median_diff</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cluster</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3</td>\n",
       "      <td>-1.266667</td>\n",
       "      <td>7.295652</td>\n",
       "      <td>131146.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9</td>\n",
       "      <td>-0.866667</td>\n",
       "      <td>0.073430</td>\n",
       "      <td>232993.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>9</td>\n",
       "      <td>-0.777778</td>\n",
       "      <td>-18.926570</td>\n",
       "      <td>107782.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>5</td>\n",
       "      <td>-0.700000</td>\n",
       "      <td>33.228986</td>\n",
       "      <td>-133071.0</td>\n",
       "      <td>-4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>-0.575000</td>\n",
       "      <td>-2.746014</td>\n",
       "      <td>-142143.0</td>\n",
       "      <td>-1.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Count  your_imdb_Rating_Mean_Difference  RunTime_Mean_Difference  \\\n",
       "Cluster                                                                     \n",
       "12           3                         -1.266667                 7.295652   \n",
       "6            9                         -0.866667                 0.073430   \n",
       "84           9                         -0.777778               -18.926570   \n",
       "20           5                         -0.700000                33.228986   \n",
       "1            8                         -0.575000                -2.746014   \n",
       "\n",
       "         votes_Median_Difference  Year_Median_diff  \n",
       "Cluster                                             \n",
       "12                      131146.0               3.0  \n",
       "6                       232993.0               3.0  \n",
       "84                      107782.0               0.0  \n",
       "20                     -133071.0              -4.0  \n",
       "1                      -142143.0              -1.5  "
      ]
     },
     "execution_count": 531,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Only Keeping clusters with atleast 3 movies\n",
    "cluster_stat_large = data_film_cluster_stats.loc[(data_film_cluster_stats['Count'] >=3)]\n",
    "cluster_stat_large.sort_values('your_imdb_Rating_Mean_Difference').head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Count</th>\n",
       "      <th>your_imdb_Rating_Mean_Difference</th>\n",
       "      <th>RunTime_Mean_Difference</th>\n",
       "      <th>votes_Median_Difference</th>\n",
       "      <th>Year_Median_diff</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cluster</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>21</td>\n",
       "      <td>0.504762</td>\n",
       "      <td>-0.894824</td>\n",
       "      <td>-47522.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.525000</td>\n",
       "      <td>-26.371014</td>\n",
       "      <td>-34436.5</td>\n",
       "      <td>-2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>4</td>\n",
       "      <td>0.525000</td>\n",
       "      <td>30.628986</td>\n",
       "      <td>764168.0</td>\n",
       "      <td>-13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>3</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>-21.037681</td>\n",
       "      <td>90756.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>3</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>-14.371014</td>\n",
       "      <td>203581.0</td>\n",
       "      <td>-3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Count  your_imdb_Rating_Mean_Difference  RunTime_Mean_Difference  \\\n",
       "Cluster                                                                     \n",
       "46          21                          0.504762                -0.894824   \n",
       "4            4                          0.525000               -26.371014   \n",
       "27           4                          0.525000                30.628986   \n",
       "17           3                          0.533333               -21.037681   \n",
       "51           3                          0.666667               -14.371014   \n",
       "\n",
       "         votes_Median_Difference  Year_Median_diff  \n",
       "Cluster                                             \n",
       "46                      -47522.0              -1.0  \n",
       "4                       -34436.5              -2.5  \n",
       "27                      764168.0             -13.0  \n",
       "17                       90756.0               1.0  \n",
       "51                      203581.0              -3.0  "
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_stat_large.sort_values('your_imdb_Rating_Mean_Difference').tail(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Clusters of Interest on the basis of Mean difference in Runtime(Length)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Count</th>\n",
       "      <th>your_imdb_Rating_Mean_Difference</th>\n",
       "      <th>RunTime_Mean_Difference</th>\n",
       "      <th>votes_Median_Difference</th>\n",
       "      <th>Year_Median_diff</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cluster</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>3</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>-30.704348</td>\n",
       "      <td>-16223.0</td>\n",
       "      <td>-26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>9</td>\n",
       "      <td>-0.122222</td>\n",
       "      <td>-29.148792</td>\n",
       "      <td>-183258.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3</td>\n",
       "      <td>-0.133333</td>\n",
       "      <td>-28.037681</td>\n",
       "      <td>578074.0</td>\n",
       "      <td>-4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.525000</td>\n",
       "      <td>-26.371014</td>\n",
       "      <td>-34436.5</td>\n",
       "      <td>-2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>3</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>-24.037681</td>\n",
       "      <td>709233.0</td>\n",
       "      <td>-5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Count  your_imdb_Rating_Mean_Difference  RunTime_Mean_Difference  \\\n",
       "Cluster                                                                     \n",
       "18           3                          0.133333               -30.704348   \n",
       "59           9                         -0.122222               -29.148792   \n",
       "8            3                         -0.133333               -28.037681   \n",
       "4            4                          0.525000               -26.371014   \n",
       "43           3                          0.066667               -24.037681   \n",
       "\n",
       "         votes_Median_Difference  Year_Median_diff  \n",
       "Cluster                                             \n",
       "18                      -16223.0             -26.0  \n",
       "59                     -183258.0               3.0  \n",
       "8                       578074.0              -4.0  \n",
       "4                       -34436.5              -2.5  \n",
       "43                      709233.0              -5.0  "
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_stat_large = data_film_cluster_stats.loc[(data_film_cluster_stats['Count'] >=3)]\n",
    "cluster_stat_large.sort_values('RunTime_Mean_Difference').head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Count</th>\n",
       "      <th>your_imdb_Rating_Mean_Difference</th>\n",
       "      <th>RunTime_Mean_Difference</th>\n",
       "      <th>votes_Median_Difference</th>\n",
       "      <th>Year_Median_diff</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cluster</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>-0.533333</td>\n",
       "      <td>22.462319</td>\n",
       "      <td>1224601.5</td>\n",
       "      <td>-2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>4</td>\n",
       "      <td>0.525000</td>\n",
       "      <td>30.628986</td>\n",
       "      <td>764168.0</td>\n",
       "      <td>-13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>5</td>\n",
       "      <td>-0.700000</td>\n",
       "      <td>33.228986</td>\n",
       "      <td>-133071.0</td>\n",
       "      <td>-4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>3</td>\n",
       "      <td>-0.033333</td>\n",
       "      <td>36.962319</td>\n",
       "      <td>-176247.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>3</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>73.962319</td>\n",
       "      <td>-123867.0</td>\n",
       "      <td>-9.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Count  your_imdb_Rating_Mean_Difference  RunTime_Mean_Difference  \\\n",
       "Cluster                                                                     \n",
       "3            6                         -0.533333                22.462319   \n",
       "27           4                          0.525000                30.628986   \n",
       "20           5                         -0.700000                33.228986   \n",
       "32           3                         -0.033333                36.962319   \n",
       "33           3                          0.333333                73.962319   \n",
       "\n",
       "         votes_Median_Difference  Year_Median_diff  \n",
       "Cluster                                             \n",
       "3                      1224601.5              -2.0  \n",
       "27                      764168.0             -13.0  \n",
       "20                     -133071.0              -4.0  \n",
       "32                     -176247.0               2.0  \n",
       "33                     -123867.0              -9.0  "
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_stat_large.sort_values('RunTime_Mean_Difference').tail(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Clusters of Interest on the basis of Median Num Votes(Popularity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Count</th>\n",
       "      <th>your_imdb_Rating_Mean_Difference</th>\n",
       "      <th>RunTime_Mean_Difference</th>\n",
       "      <th>votes_Median_Difference</th>\n",
       "      <th>Year_Median_diff</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cluster</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>-12.371014</td>\n",
       "      <td>-191972.5</td>\n",
       "      <td>-56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>3</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>10.628986</td>\n",
       "      <td>-187085.0</td>\n",
       "      <td>-5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>9</td>\n",
       "      <td>-0.122222</td>\n",
       "      <td>-29.148792</td>\n",
       "      <td>-183258.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>3</td>\n",
       "      <td>-0.033333</td>\n",
       "      <td>36.962319</td>\n",
       "      <td>-176247.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>3</td>\n",
       "      <td>-0.300000</td>\n",
       "      <td>-1.037681</td>\n",
       "      <td>-175945.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Count  your_imdb_Rating_Mean_Difference  RunTime_Mean_Difference  \\\n",
       "Cluster                                                                     \n",
       "9            4                          0.150000               -12.371014   \n",
       "39           3                          0.466667                10.628986   \n",
       "59           9                         -0.122222               -29.148792   \n",
       "32           3                         -0.033333                36.962319   \n",
       "26           3                         -0.300000                -1.037681   \n",
       "\n",
       "         votes_Median_Difference  Year_Median_diff  \n",
       "Cluster                                             \n",
       "9                      -191972.5             -56.0  \n",
       "39                     -187085.0              -5.0  \n",
       "59                     -183258.0               3.0  \n",
       "32                     -176247.0               2.0  \n",
       "26                     -175945.0               2.0  "
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_stat_large = data_film_cluster_stats.loc[(data_film_cluster_stats['Count'] >=3)]\n",
    "cluster_stat_large.sort_values('votes_Median_Difference').head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Count</th>\n",
       "      <th>your_imdb_Rating_Mean_Difference</th>\n",
       "      <th>RunTime_Mean_Difference</th>\n",
       "      <th>votes_Median_Difference</th>\n",
       "      <th>Year_Median_diff</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cluster</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>3</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>-24.037681</td>\n",
       "      <td>709233.0</td>\n",
       "      <td>-5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>4</td>\n",
       "      <td>0.525000</td>\n",
       "      <td>30.628986</td>\n",
       "      <td>764168.0</td>\n",
       "      <td>-13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>3</td>\n",
       "      <td>-0.566667</td>\n",
       "      <td>13.628986</td>\n",
       "      <td>790919.0</td>\n",
       "      <td>-19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>4</td>\n",
       "      <td>-0.050000</td>\n",
       "      <td>11.378986</td>\n",
       "      <td>887431.0</td>\n",
       "      <td>-8.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>-0.533333</td>\n",
       "      <td>22.462319</td>\n",
       "      <td>1224601.5</td>\n",
       "      <td>-2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Count  your_imdb_Rating_Mean_Difference  RunTime_Mean_Difference  \\\n",
       "Cluster                                                                     \n",
       "43           3                          0.066667               -24.037681   \n",
       "27           4                          0.525000                30.628986   \n",
       "15           3                         -0.566667                13.628986   \n",
       "40           4                         -0.050000                11.378986   \n",
       "3            6                         -0.533333                22.462319   \n",
       "\n",
       "         votes_Median_Difference  Year_Median_diff  \n",
       "Cluster                                             \n",
       "43                      709233.0              -5.0  \n",
       "27                      764168.0             -13.0  \n",
       "15                      790919.0             -19.0  \n",
       "40                      887431.0              -8.5  \n",
       "3                      1224601.5              -2.0  "
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_stat_large.sort_values('votes_Median_Difference').tail(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Clusters of Interest on the basis of Median Year of Release."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "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>Count</th>\n",
       "      <th>your_imdb_Rating_Mean_Difference</th>\n",
       "      <th>RunTime_Mean_Difference</th>\n",
       "      <th>votes_Median_Difference</th>\n",
       "      <th>Year_Median_diff</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cluster</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>-12.371014</td>\n",
       "      <td>-191972.5</td>\n",
       "      <td>-56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>3</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>-30.704348</td>\n",
       "      <td>-16223.0</td>\n",
       "      <td>-26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>3</td>\n",
       "      <td>-0.566667</td>\n",
       "      <td>13.628986</td>\n",
       "      <td>790919.0</td>\n",
       "      <td>-19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>4</td>\n",
       "      <td>0.525000</td>\n",
       "      <td>30.628986</td>\n",
       "      <td>764168.0</td>\n",
       "      <td>-13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>3</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>73.962319</td>\n",
       "      <td>-123867.0</td>\n",
       "      <td>-9.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Count  your_imdb_Rating_Mean_Difference  RunTime_Mean_Difference  \\\n",
       "Cluster                                                                     \n",
       "9            4                          0.150000               -12.371014   \n",
       "18           3                          0.133333               -30.704348   \n",
       "15           3                         -0.566667                13.628986   \n",
       "27           4                          0.525000                30.628986   \n",
       "33           3                          0.333333                73.962319   \n",
       "\n",
       "         votes_Median_Difference  Year_Median_diff  \n",
       "Cluster                                             \n",
       "9                      -191972.5             -56.0  \n",
       "18                      -16223.0             -26.0  \n",
       "15                      790919.0             -19.0  \n",
       "27                      764168.0             -13.0  \n",
       "33                     -123867.0              -9.0  "
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_stat_large = data_film_cluster_stats.loc[(data_film_cluster_stats['Count'] >=3)]\n",
    "cluster_stat_large.sort_values('Year_Median_diff').head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 508,
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped_df = data_film_orig.groupby(['Cluster'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interpreting Largest Clusters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cluster 73 - Cluster of Newer Action-Adventure, Drama, Sci-Fi , Superhero movies.\n",
    "1. 25 Films.\n",
    "2. Rated Less favourably.\n",
    "3. Newer Films, reasonably popular.\n",
    "4. Statistically Significant group compared to all Your Ratings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 430,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Population Mean 7.58840579710145, degrees of freedom 345\n",
      "Sample mean 7.12, degrees of freedom 25\n",
      "\n",
      "T-Stat for hypothesis test -3.227188621192152 and P-value 0.0035957593287332637\n",
      "T-critical for n = 24, alpha = 0.05 , look up in table\n",
      "Statistically significant result, Reject the NUll - \n",
      "There is siginificant difference between ratings given to films in this group and the population.\n"
     ]
    }
   ],
   "source": [
    "#grouped_df.get_group(73).head()\n",
    "ttest_1samp_cluster_pop(73)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cluster 46 - Highly Rated Drama, Dramas with comedy, Romantic elements.\n",
    "1. 21 Films.\n",
    "2. Rated Much more favourably, author clearly prefers these genres.\n",
    "3. Less popular than the median.\n",
    "4. Statistically Significant Result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 503,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Population Mean 7.58840579710145, degrees of freedom 345\n",
      "Sample mean 8.333333333333334, degrees of freedom 21\n",
      "\n",
      "T-Stat for hypothesis test 4.289508201285393 and P-value 0.0003575218358831785\n",
      "T-critical for n = 20, alpha = 0.05 , look up in table\n",
      "Statistically significant result, Reject the NUll - \n",
      "There is siginificant difference between ratings given to films in this group and the population.\n"
     ]
    }
   ],
   "source": [
    "#grouped_df.get_group(46).head(100)\n",
    "ttest_1samp_cluster_pop(46)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cluster 62 - Cluster of Newer Romantic Comedies, Dramas.\n",
    "1. 25 Films.\n",
    "2. Rated about the same as IMDb Users.\n",
    "3. Newer Films, much less popular than median.\n",
    "4. Statistically Insignificant Result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "metadata": {},
   "outputs": [],
   "source": [
    "#grouped_df.get_group(62).head(100)\n",
    "#ttest_1samp_cluster_pop(62)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cluster 61 - Cluster of Newer Dramas.\n",
    "1. 22 Films.\n",
    "2. Rated Less favourably.\n",
    "3. Newer Films, not popular.\n",
    "4. Statistically Insignificant Result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "metadata": {},
   "outputs": [],
   "source": [
    "#grouped_df.get_group(61).head(100)\n",
    "#ttest_1samp_cluster_pop(61)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cluster 0 - Cluster of Acclaimed Biographies.\n",
    "1. 21 Films.\n",
    "2. Rated about the same as IMDb users.\n",
    "3. Popular Films\n",
    "4. Statistically Insignificant Result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 441,
   "metadata": {},
   "outputs": [],
   "source": [
    "#grouped_df.get_group(0).head(100)\n",
    "#ttest_1samp_cluster_pop(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Interpreting Clusters with Skewed Satistics."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Statistifically Significant Clusters by Your Rating - 1, 5, 40, 46, 73"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cluster 1 - Lower Rated Comedy, Drama, Musicals\n",
    "1. 8 Films.\n",
    "2. Rated lower than IMDb Users.\n",
    "3. Statistically Significant Result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 439,
   "metadata": {},
   "outputs": [],
   "source": [
    "#grouped_df.get_group(1)\n",
    "#ttest_1samp_cluster_pop(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cluster 5 - Lower Rated Comedy, Hindi Comedies\n",
    "1. 19 Films.\n",
    "2. Rated lower than IMDb Users.\n",
    "3. Statistically Significant Result. - Rated lower than mean ratings\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 443,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Population Mean 7.58840579710145, degrees of freedom 345\n",
      "Sample mean 7.052631578947368, degrees of freedom 19\n",
      "\n",
      "T-Stat for hypothesis test -2.2762523875302234 and P-value 0.03528088015291208\n",
      "T-critical for n = 18, alpha = 0.05 , look up in table\n",
      "Statistically significant result, Reject the NUll - \n",
      "There is siginificant difference between ratings given to films in this group and the population.\n"
     ]
    }
   ],
   "source": [
    "#grouped_df.get_group(5)\n",
    "#ttest_1samp_cluster_pop(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cluster 6 - Crime/Thriller/ Horror movies.\n",
    "1. 9 Films.\n",
    "2. Crime/Thriller/ Horror movies.\n",
    "3. Rated much Lower than IMDb Users.\n",
    "4. Statistically Insignificant Result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 512,
   "metadata": {},
   "outputs": [],
   "source": [
    "#grouped_df.get_group(6).head(3)\n",
    "#ttest_1samp_cluster_pop(6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cluster 12 - Horror Movies by James Wan.\n",
    "1. 3 Films.\n",
    "2. Horror Movies by James Wan.\n",
    "3. Rated much Lower than IMDb Users.\n",
    "4. Statistically Insignificant Result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {},
   "outputs": [],
   "source": [
    "#grouped_df.get_group(12)\n",
    "#ttest_1samp_cluster_pop(12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cluster 84 - Animated Movies rated less favourably.\n",
    "1. 9 Films.\n",
    "2. Animated Movies\n",
    "3. Rated much Lower than IMDb Users.\n",
    "4. Statistically Insignificant Result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "metadata": {},
   "outputs": [],
   "source": [
    "#grouped_df.get_group(84).head(3)\n",
    "#ttest_1samp_cluster_pop(84)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### However, Cluster 25, Cluster 43  and 17 -Animated movies Rated Favourably.\n",
    "1. 3,3,3, Films repectively.\n",
    "2. Correctly Identified clusters based on preferred directors and series.(These are by Directors Brad bird, Andrew Stanton, Dean DeBlois, How to Train Your Dragon series is a personal Favourite.)\n",
    "3. Rated Higher than IMDb Users.\n",
    "4. Statistically Insignificant Result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 405,
   "metadata": {},
   "outputs": [],
   "source": [
    "#grouped_df.get_group(25)\n",
    "#ttest_1samp_cluster_pop(17)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cluster 20 - Movies by Rajkumar Hirani\n",
    "1. 5 Films.\n",
    "2. Rated lower than IMDb users.\n",
    "3. Statistically Insignificant Result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "metadata": {},
   "outputs": [],
   "source": [
    "#grouped_df.get_group(20)\n",
    "#ttest_1samp_cluster_pop(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cluster 51 - Edgar Wright Comedies.\n",
    "1. 3 Films.\n",
    "2. Rated Much Higher than IMDb users.\n",
    "3. Statistically Insignificant Result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "metadata": {},
   "outputs": [],
   "source": [
    "#grouped_df.get_group(51)\n",
    "#ttest_1samp_cluster_pop(51)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cluster 27 -Films by  Steven Spielberg.\n",
    "1. 3 Films.\n",
    "2. Rated Much more favourably.\n",
    "3. Statistically Insignificant Result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "metadata": {},
   "outputs": [],
   "source": [
    "#grouped_df.get_group(27)\n",
    "#ttest_1samp_cluster_pop(27)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cluster 4 - Films by Wes Anderson.\n",
    "1. 4 Films.\n",
    "2. Rated Much more Favourably.\n",
    "3. Statistically Insignificant Result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "metadata": {},
   "outputs": [],
   "source": [
    "#grouped_df.get_group(4)\n",
    "#ttest_1samp_cluster_pop(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cluster 59 - Shorter Documentaries, Niche preference of author.\n",
    "1. 9 Films.\n",
    "2. Rated about the same as IMDb users.\n",
    "3. Much less popular. (Documentaries in general)\n",
    "4. Another cluster just for Formula 1  documentaries."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cluster 32,32,20 - Films way longer than the average films.\n",
    "1. 3,3,5 films.\n",
    "2. Movies by Ashutosh Gowarikor, Zoya Akhtar, Rajkumar Hirani.\n",
    "3. Popular films. \n",
    "4. Not rated particular highly.\n",
    "5. Statistically Insignificant Result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "metadata": {},
   "outputs": [],
   "source": [
    "#grouped_df.get_group(20)\n",
    "#ttest_1samp_cluster_pop(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cluster 39 - Niche Dibakar Banerjee Films.\n",
    "1. 3 Films.\n",
    "2. Rated more highly compared to IMDb users.\n",
    "3. Very niche.\n",
    "4. Statistically Insignificant Result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "metadata": {},
   "outputs": [],
   "source": [
    "#grouped_df.get_group(39)\n",
    "#ttest_1samp_cluster_pop(39)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cluster 9 - Niche, Prestige Satyajit Ray Films.\n",
    "1. 4 Films.\n",
    "2. Generally Rated very highly.\n",
    "3. Very niche and the cluster with the oldest mean year release.\n",
    "4. Statistically Insignificant Result. Almost significant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 446,
   "metadata": {},
   "outputs": [],
   "source": [
    "#grouped_df.get_group(9)\n",
    "#ttest_1samp_cluster_pop(9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cluster 15,27 - Favourable clusters based on directors.\n",
    "1. 3, 4 Films.\n",
    "2. Films by Rob Reiner, Steven Spielberg. \n",
    "3. Rated very highly.\n",
    "4. Statistically Insignificant Result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "metadata": {},
   "outputs": [],
   "source": [
    "#grouped_df.get_group(27)\n",
    "#ttest_1samp_cluster_pop(27)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cluster 76 - Cluster of popular Adventure Movies.\n",
    "1. 6 Films.\n",
    "2. Rated slighly higher.\n",
    "3. Statistically Insignificant Result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 391,
   "metadata": {},
   "outputs": [],
   "source": [
    "#grouped_df.get_group(76)\n",
    "#ttest_1samp_cluster_pop(76)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x121137ac8>"
      ]
     },
     "execution_count": 395,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,6))\n",
    "sns.boxplot(x = 'Cluster', y = 'Your Rating', data = data_film_orig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 396,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x129d29668>"
      ]
     },
     "execution_count": 396,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,6))\n",
    "sns.boxplot(x = 'Cluster', y = 'IMDb Rating', data = data_film_orig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x129d29588>"
      ]
     },
     "execution_count": 397,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,6))\n",
    "sns.boxplot(x = 'Cluster', y = 'Year', data = data_film_orig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12bb20be0>"
      ]
     },
     "execution_count": 398,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,6))\n",
    "sns.boxplot(x = 'Cluster', y = 'Num Votes', data = data_film_orig)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}