Machine learning -TP2 (cours).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = \"Data.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = pd.read_csv(path) # importer dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Age</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Purchased</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>France</td>\n",
       "      <td>44.0</td>\n",
       "      <td>72000.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Spain</td>\n",
       "      <td>27.0</td>\n",
       "      <td>48000.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Germany</td>\n",
       "      <td>30.0</td>\n",
       "      <td>54000.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Spain</td>\n",
       "      <td>38.0</td>\n",
       "      <td>61000.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Germany</td>\n",
       "      <td>40.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   Country   Age   Salary Purchased\n",
       "0   France  44.0  72000.0        No\n",
       "1    Spain  27.0  48000.0       Yes\n",
       "2  Germany  30.0  54000.0        No\n",
       "3    Spain  38.0  61000.0        No\n",
       "4  Germany  40.0      NaN       Yes"
      ]
     },
     "execution_count": 5,
     "metadata": {},
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    }
   ],
   "source": [
    "ds.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = ds.iloc[:,:-1].values\n",
    "y = ds.iloc[:,-1:].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Country  Age  Salary\n",
      "[['France' 44.0 72000.0]\n",
      " ['Spain' 27.0 48000.0]\n",
      " ['Germany' 30.0 54000.0]\n",
      " ['Spain' 38.0 61000.0]\n",
      " ['Germany' 40.0 nan]\n",
      " ['France' 35.0 58000.0]\n",
      " ['Spain' nan 52000.0]\n",
      " ['France' 48.0 79000.0]\n",
      " ['Germany' 50.0 83000.0]\n",
      " ['France' 37.0 67000.0]]\n"
     ]
    }
   ],
   "source": [
    "print(\"   Country  Age  Salary\")\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Purchased\n",
      "['No' 'Yes' 'No' 'No' 'Yes' 'Yes' 'No' 'Yes' 'No' 'Yes']\n"
     ]
    }
   ],
   "source": [
    "print(\"Purchased\")\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Remplacer NaN par la moyenne"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\elitebook\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import Imputer\n",
    "imp = Imputer(missing_values='NaN', strategy='mean')\n",
    "imp.fit(x[:,1:3])\n",
    "x[:,1:3] = imp.transform(x[:,1:3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Remplacer "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\elitebook\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:415: FutureWarning: The handling of integer data will change in version 0.22. Currently, the categories are determined based on the range [0, max(values)], while in the future they will be determined based on the unique values.\n",
      "If you want the future behaviour and silence this warning, you can specify \"categories='auto'\".\n",
      "In case you used a LabelEncoder before this OneHotEncoder to convert the categories to integers, then you can now use the OneHotEncoder directly.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\elitebook\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:451: DeprecationWarning: The 'categorical_features' keyword is deprecated in version 0.20 and will be removed in 0.22. You can use the ColumnTransformer instead.\n",
      "  \"use the ColumnTransformer instead.\", DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<10x5 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 30 stored elements in COOrdinate format>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n",
    "label_x = LabelEncoder()\n",
    "x[:,0] = label_x.fit_transform(x[:,0])\n",
    "xonehot = OneHotEncoder(categorical_features=[0])\n",
    "x = xonehot.fit_transform(x)\n",
    "x\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\elitebook\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:415: FutureWarning: The handling of integer data will change in version 0.22. Currently, the categories are determined based on the range [0, max(values)], while in the future they will be determined based on the unique values.\n",
      "If you want the future behaviour and silence this warning, you can specify \"categories='auto'\".\n",
      "In case you used a LabelEncoder before this OneHotEncoder to convert the categories to integers, then you can now use the OneHotEncoder directly.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\elitebook\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:451: DeprecationWarning: The 'categorical_features' keyword is deprecated in version 0.20 and will be removed in 0.22. You can use the ColumnTransformer instead.\n",
      "  \"use the ColumnTransformer instead.\", DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[1., 0.],\n",
       "       [0., 1.],\n",
       "       [1., 0.],\n",
       "       [1., 0.],\n",
       "       [0., 1.],\n",
       "       [0., 1.],\n",
       "       [1., 0.],\n",
       "       [0., 1.],\n",
       "       [1., 0.],\n",
       "       [0., 1.]])"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_y = LabelEncoder()\n",
    "y[:, 0] = label_y.fit_transform(y[:, 0])\n",
    "yonehot = OneHotEncoder(categorical_features=[0])\n",
    "y = yonehot.fit_transform(y).toarray()\n",
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Deviseer le dataset entre le training et le test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['Germany', 40.0, nan],\n",
       "       ['France', 37.0, 67000.0],\n",
       "       ['Spain', 27.0, 48000.0],\n",
       "       ['Spain', nan, 52000.0],\n",
       "       ['France', 48.0, 79000.0],\n",
       "       ['Spain', 38.0, 61000.0],\n",
       "       ['France', 44.0, 72000.0],\n",
       "       ['France', 35.0, 58000.0]], dtype=object)"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "#help(train_test_split)\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.2, random_state = 0)\n",
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Age</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>France</td>\n",
       "      <td>44</td>\n",
       "      <td>72000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Spain</td>\n",
       "      <td>27</td>\n",
       "      <td>48000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Germany</td>\n",
       "      <td>30</td>\n",
       "      <td>54000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Spain</td>\n",
       "      <td>38</td>\n",
       "      <td>61000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Germany</td>\n",
       "      <td>40</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>France</td>\n",
       "      <td>35</td>\n",
       "      <td>58000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Spain</td>\n",
       "      <td>NaN</td>\n",
       "      <td>52000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>France</td>\n",
       "      <td>48</td>\n",
       "      <td>79000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>Germany</td>\n",
       "      <td>50</td>\n",
       "      <td>83000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>France</td>\n",
       "      <td>37</td>\n",
       "      <td>67000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Country  Age Salary\n",
       "0   France   44  72000\n",
       "1    Spain   27  48000\n",
       "2  Germany   30  54000\n",
       "3    Spain   38  61000\n",
       "4  Germany   40    NaN\n",
       "5   France   35  58000\n",
       "6    Spain  NaN  52000\n",
       "7   France   48  79000\n",
       "8  Germany   50  83000\n",
       "9   France   37  67000"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = pd.DataFrame(data=x, columns=[\"Country\",  \"Age\",  \"Salary\"])\n",
    "x\n",
    "\n",
    "y = pd.DataFrame(data=y, columns=[\"YesOrNo\"])\n",
    "y\n",
    "#help( pd.DataFrame)\n",
    "y[\"code\"] = y.YesOrNo.replace({\"Yes\": 1, \"No\": 0})\n",
    "x"
   ]
  }
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