customer_behaviour.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e6e17d54",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Random Forest Classification, decision tree classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a2056219",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfc095f6",
   "metadata": {},
   "source": [
    "# To buy or not to buy?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e19153f6",
   "metadata": {},
   "source": [
    "This dataset contains information about the age, gender and annual salary of people interested in buying in car and whether or not they bought the car. The data will be used to train a model to help predict if future customers will buy the car or not.  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b282264",
   "metadata": {},
   "source": [
    "### The trained model\n",
    "The model trained below can be used to predicted whether or not someone will purchase a car based on their age and annual salary. This can be useful in a number of ways. For example, a car dealership can use this model to predict whether a potential customer is likely to purchase a car based on their age and salary, and then target their advertising and sales efforts accordingly. This can help the dealership to more efficiently allocate their resources and increase their sales. Similarly, an auto manufacturer can use this model to gain insights into their target market and tailor their product offerings to better meet the needs of their customers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "910a1fdb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('car_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "854052e7",
   "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>User ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>AnnualSalary</th>\n",
       "      <th>Purchased</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>385</td>\n",
       "      <td>Male</td>\n",
       "      <td>35</td>\n",
       "      <td>20000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>681</td>\n",
       "      <td>Male</td>\n",
       "      <td>40</td>\n",
       "      <td>43500</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>353</td>\n",
       "      <td>Male</td>\n",
       "      <td>49</td>\n",
       "      <td>74000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>Male</td>\n",
       "      <td>40</td>\n",
       "      <td>107500</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>661</td>\n",
       "      <td>Male</td>\n",
       "      <td>25</td>\n",
       "      <td>79000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   User ID Gender  Age  AnnualSalary  Purchased\n",
       "0      385   Male   35         20000          0\n",
       "1      681   Male   40         43500          0\n",
       "2      353   Male   49         74000          0\n",
       "3      895   Male   40        107500          1\n",
       "4      661   Male   25         79000          0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(df.head(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b9323d78",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "X = df.iloc[:400, [2,3]].values\n",
    "y = df.iloc[:400, -1].values\n",
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "bcb6d0fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      " [    39 122000]\n",
      " [    56  84500]]\n"
     ]
    }
   ],
   "source": [
    "sampled_df = df.sample(n=400)\n",
    "X = sampled_df.iloc[:, [2,3]].values\n",
    "y = sampled_df.iloc[:, -1].values\n",
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "20b575af",
   "metadata": {},
   "outputs": [],
   "source": [
    "#splitting the dataset into the Training set and Test set\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size = 0.25, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "49edbaa8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#feature scaling\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "classifier = LogisticRegression()\n",
    "sc = StandardScaler()\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.transform(X_test)\n",
    "y_train = y_train.ravel()\n",
    "classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "31ea61ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.86185204  2.03821389]\n",
      " [ 0.10108128 -0.01879763]\n",
      " [ 0.10108128 -0.31265642]\n",
      " [ 0.00326069 -0.4385959 ]\n",
      " [-0.77930409 -1.53007141]\n",
      " [ 0.29672248  1.03069804]\n",
      " [-0.6814835   0.80680563]\n",
      " [ 1.56839025  0.94673839]\n",
      " [ 0.88364606 -1.15225296]\n",
      " [-0.3880217   0.02318219]\n",
      " [-1.26840708  0.2470746 ]\n",
      " [ 1.07928726  0.72284598]\n",
      " [ 0.19890188 -0.52255556]\n",
      " [-0.09455991  0.2470746 ]\n",
      " [-0.3880217  -0.59252194]\n",
      " [-0.77930409  0.0791553 ]\n",
      " [ 0.88364606 -1.06829331]\n",
      " [ 0.00326069 -0.4385959 ]\n",
      " [-1.75751007 -1.54406468]\n",
      " [-1.26840708  0.6528796 ]\n",
      " [-0.29020111  2.05220717]\n",
      " [-2.05097186 -1.44611175]\n",
      " [ 1.07928726  1.70237528]\n",
      " [ 1.27492845 -1.40413193]\n",
      " [-0.3880217   0.10714185]\n",
      " [-0.5836629  -1.20822607]\n",
      " [-2.14879246 -0.57852866]\n",
      " [-0.29020111  0.06516202]\n",
      " [-0.29020111 -0.29866315]\n",
      " [-0.3880217  -0.34064297]\n",
      " [-0.4858423  -1.4740983 ]\n",
      " [-0.4858423  -0.3826228 ]\n",
      " [-0.09455991  0.12113512]\n",
      " [-0.77930409  1.32455683]\n",
      " [ 0.00326069  0.27506116]\n",
      " [-0.3880217   1.46448959]\n",
      " [ 1.47056965 -1.26419917]\n",
      " [-0.19238051  0.1911015 ]\n",
      " [ 0.00326069  1.42250976]\n",
      " [-1.65968947 -1.23621262]\n",
      " [ 0.19890188  0.72284598]\n",
      " [-1.75751007  0.23308133]\n",
      " [ 1.86185204  0.94673839]\n",
      " [-0.4858423   0.00918892]\n",
      " [-1.56186887  0.45697374]\n",
      " [ 1.27492845 -1.08228659]\n",
      " [-1.26840708  0.42898719]\n",
      " [-0.19238051  0.2470746 ]\n",
      " [-1.26840708 -1.57205123]\n",
      " [-1.17058648  0.69485943]\n",
      " [ 0.00326069  0.2470746 ]\n",
      " [-1.07276588  0.20509478]\n",
      " [ 0.10108128 -0.3826228 ]\n",
      " [-0.4858423   0.12113512]\n",
      " [ 1.17710786  0.48496029]\n",
      " [ 0.00326069  0.06516202]\n",
      " [-1.75751007 -1.53007141]\n",
      " [ 1.07928726  0.47096702]\n",
      " [-1.07276588 -0.3266497 ]\n",
      " [ 0.00326069 -0.01879763]\n",
      " [-0.19238051  0.20509478]\n",
      " [-0.29020111 -0.22869677]\n",
      " [ 0.29672248  1.03069804]\n",
      " [ 0.29672248  0.1351284 ]\n",
      " [ 0.78582547 -1.15225296]\n",
      " [-0.97494529  0.45697374]\n",
      " [-0.3880217  -0.45258918]\n",
      " [ 0.49236367  0.17710823]\n",
      " [ 2.25313443  0.89076529]\n",
      " [-0.29020111 -0.55054211]\n",
      " [ 1.95967263 -1.23621262]\n",
      " [ 2.05749323 -0.64849504]\n",
      " [ 0.10108128  1.1706308 ]\n",
      " [ 0.00326069  0.06516202]\n",
      " [-1.17058648 -0.3826228 ]\n",
      " [ 0.59018427  1.75834838]\n",
      " [-0.97494529 -1.55805796]\n",
      " [-0.29020111 -0.53654883]\n",
      " [-0.4858423  -1.39013865]\n",
      " [-1.85533066 -0.62050849]\n",
      " [ 1.47056965  2.23411975]\n",
      " [-0.5836629  -0.8444009 ]\n",
      " [-1.75751007 -0.2706766 ]\n",
      " [ 0.29672248 -0.17272367]\n",
      " [ 0.19890188  0.87677201]\n",
      " [ 0.19890188 -0.24269004]\n",
      " [-1.36622768 -1.55805796]\n",
      " [ 0.10108128 -0.57852866]\n",
      " [ 1.27492845 -1.40413193]\n",
      " [ 0.19890188 -0.22869677]\n",
      " [-0.6814835  -0.35463625]\n",
      " [ 1.86185204 -0.67648159]\n",
      " [-0.19238051 -0.53654883]\n",
      " [-0.19238051  0.1911015 ]\n",
      " [-0.97494529 -1.20822607]\n",
      " [ 0.88364606  0.49895357]\n",
      " [ 1.86185204 -1.34815882]\n",
      " [ 0.19890188 -0.53654883]\n",
      " [-0.4858423  -0.01879763]\n",
      " [ 0.19890188 -0.56453539]\n",
      " [-0.77930409  1.7443551 ]\n",
      " [ 0.98146666 -0.76044124]\n",
      " [ 0.10108128 -0.2706766 ]\n",
      " [ 1.17710786  0.49895357]\n",
      " [ 0.98146666 -0.56453539]\n",
      " [ 0.19890188 -0.73245469]\n",
      " [ 0.88364606  0.72284598]\n",
      " [ 0.19890188  0.20509478]\n",
      " [-0.19238051 -0.50856228]\n",
      " [-0.4858423   1.2266039 ]\n",
      " [ 0.29672248 -0.48057573]\n",
      " [-0.19238051 -0.39661608]\n",
      " [ 0.78582547 -0.62050849]\n",
      " [ 0.00326069  0.1911015 ]\n",
      " [ 1.86185204  0.66687287]\n",
      " [-1.17058648  0.17710823]\n",
      " [-0.4858423  -0.4385959 ]\n",
      " [ 0.00326069 -0.48057573]\n",
      " [ 0.78582547 -0.70446814]\n",
      " [ 0.19890188 -0.00480436]\n",
      " [-1.46404827 -1.23621262]\n",
      " [ 1.66621084  0.91875184]\n",
      " [-1.26840708 -0.7884278 ]\n",
      " [-0.6814835   1.78633493]\n",
      " [-0.29020111  0.03717547]\n",
      " [-0.3880217  -0.31265642]\n",
      " [-0.19238051  1.10066442]\n",
      " [-0.3880217  -1.29218572]\n",
      " [ 0.78582547 -1.04030676]\n",
      " [ 1.07928726  1.89828113]\n",
      " [-0.19238051  0.58291322]\n",
      " [ 0.98146666  2.06620044]\n",
      " [ 1.17710786 -1.34815882]\n",
      " [-1.26840708  0.35902081]\n",
      " [-0.29020111  0.20509478]\n",
      " [ 2.25313443 -0.7884278 ]\n",
      " [ 0.59018427 -0.3826228 ]\n",
      " [ 0.88364606  1.31056356]\n",
      " [ 1.76403144 -0.87238745]\n",
      " [ 0.00326069  0.05116874]\n",
      " [-0.97494529  2.1501601 ]\n",
      " [-0.4858423   0.51294684]\n",
      " [-0.4858423   0.06516202]\n",
      " [ 1.95967263  1.45049631]\n",
      " [-0.4858423  -0.49456901]\n",
      " [-1.17058648 -0.99832693]\n",
      " [-0.4858423  -1.33416555]\n",
      " [-0.19238051 -1.34815882]\n",
      " [-0.4858423  -0.71846142]\n",
      " [-0.6814835   1.36653666]\n",
      " [ 0.78582547 -1.43211848]\n",
      " [ 0.19890188 -0.7884278 ]\n",
      " [ 1.27492845 -0.73245469]\n",
      " [ 1.86185204  1.96824751]\n",
      " [-0.6814835  -0.60651521]\n",
      " [ 1.66621084  1.73036183]\n",
      " [-0.77930409 -0.08876401]\n",
      " [-0.19238051  0.2470746 ]\n",
      " [ 0.68800487  0.30304771]\n",
      " [-0.19238051 -0.36862953]\n",
      " [ 0.49236367  1.6324089 ]\n",
      " [ 0.19890188  0.02318219]\n",
      " [-1.07276588 -0.83040762]\n",
      " [-1.17058648  1.40851649]\n",
      " [ 2.25313443 -0.7884278 ]\n",
      " [-0.09455991 -0.00480436]\n",
      " [-1.56186887 -1.39013865]\n",
      " [ 0.49236367 -1.13825969]\n",
      " [ 0.78582547 -0.59252194]\n",
      " [-0.77930409  0.37301409]\n",
      " [ 0.10108128  0.20509478]\n",
      " [ 0.19890188  2.13616682]\n",
      " [ 0.19890188  0.44298046]\n",
      " [-0.4858423  -1.27819245]\n",
      " [-1.26840708  0.02318219]\n",
      " [ 0.10108128 -0.39661608]\n",
      " [ 0.00326069  0.10714185]\n",
      " [ 1.47056965  1.2266039 ]\n",
      " [-0.4858423  -0.80242107]\n",
      " [-0.3880217   1.99623406]\n",
      " [ 0.88364606 -1.34815882]\n",
      " [-1.85533066 -0.01879763]\n",
      " [ 0.19890188 -1.15225296]\n",
      " [-1.26840708 -0.53654883]\n",
      " [-1.26840708 -1.4740983 ]\n",
      " [ 0.10108128 -0.57852866]\n",
      " [-1.17058648 -0.48057573]\n",
      " [ 0.19890188  0.1911015 ]\n",
      " [ 2.15531383  1.67438872]\n",
      " [-1.46404827 -0.45258918]\n",
      " [ 1.37274905  1.78633493]\n",
      " [-1.36622768 -1.61403106]\n",
      " [ 0.78582547  1.91227441]\n",
      " [ 1.47056965 -1.37614537]\n",
      " [ 1.76403144 -0.71846142]\n",
      " [-1.17058648 -0.39661608]\n",
      " [ 0.10108128  0.02318219]\n",
      " [-0.29020111  1.53445597]\n",
      " [-0.87712469 -1.57205123]\n",
      " [ 0.10108128 -0.04678419]\n",
      " [ 1.66621084  0.03717547]\n",
      " [ 0.39454308  0.05116874]\n",
      " [-0.09455991 -0.3826228 ]\n",
      " [ 1.07928726  0.55492667]\n",
      " [-0.87712469 -0.13074384]\n",
      " [ 0.68800487  1.92626769]\n",
      " [-0.09455991 -0.04678419]\n",
      " [ 1.47056965  1.46448959]\n",
      " [ 0.29672248 -0.03279091]\n",
      " [ 0.19890188 -0.31265642]\n",
      " [ 0.78582547 -1.11027314]\n",
      " [ 1.07928726 -0.98433366]\n",
      " [-0.3880217  -0.39661608]\n",
      " [ 0.49236367  0.0791553 ]\n",
      " [ 0.10108128  0.17710823]\n",
      " [-0.3880217   1.03069804]\n",
      " [-0.4858423   2.08019372]\n",
      " [-0.09455991  2.06620044]\n",
      " [ 0.98146666  0.97472494]\n",
      " [-1.26840708  1.31056356]\n",
      " [-0.4858423  -0.97034038]\n",
      " [-0.29020111  0.12113512]\n",
      " [-0.09455991  1.38052993]\n",
      " [-1.26840708 -0.4385959 ]\n",
      " [ 0.98146666  1.95425424]\n",
      " [-0.87712469  0.03717547]\n",
      " [ 0.00326069  0.63888632]\n",
      " [-2.05097186  0.09314857]\n",
      " [ 1.47056965  1.89828113]\n",
      " [ 0.39454308  1.73036183]\n",
      " [-0.97494529  0.33103426]\n",
      " [-1.65968947 -1.48809158]\n",
      " [ 0.00326069  0.27506116]\n",
      " [ 0.68800487 -1.20822607]\n",
      " [ 0.78582547  1.00271149]\n",
      " [ 1.95967263 -0.85839417]\n",
      " [ 0.29672248  1.10066442]\n",
      " [-0.29020111 -0.57852866]\n",
      " [-1.75751007 -0.49456901]\n",
      " [-1.56186887 -1.20822607]\n",
      " [ 0.10108128  0.10714185]\n",
      " [ 2.25313443  0.35902081]\n",
      " [ 0.88364606 -1.01232021]\n",
      " [ 2.25313443 -1.01232021]\n",
      " [ 0.29672248  1.688382  ]\n",
      " [-2.05097186 -1.50208486]\n",
      " [-1.85533066  0.30304771]\n",
      " [-0.4858423  -0.7884278 ]\n",
      " [ 0.10108128 -1.19423279]\n",
      " [ 0.00326069 -0.35463625]\n",
      " [ 0.59018427 -1.37614537]\n",
      " [ 0.29672248  0.21908805]\n",
      " [ 0.10108128 -0.39661608]\n",
      " [-1.65968947 -0.2706766 ]\n",
      " [-0.3880217   1.49247614]\n",
      " [ 1.56839025  1.64640217]\n",
      " [-1.46404827 -1.09627986]\n",
      " [-1.36622768 -1.13825969]\n",
      " [-0.77930409  0.49895357]\n",
      " [-0.3880217  -0.36862953]\n",
      " [-1.07276588 -0.76044124]\n",
      " [ 0.59018427 -1.13825969]\n",
      " [ 0.88364606  1.91227441]\n",
      " [ 0.49236367  1.05868459]\n",
      " [-0.4858423  -0.63450176]\n",
      " [ 0.68800487 -1.34815882]\n",
      " [ 1.27492845  1.19861735]\n",
      " [ 1.17710786  2.16415337]\n",
      " [ 1.27492845 -0.92836055]\n",
      " [-1.26840708 -0.41060935]\n",
      " [ 1.37274905  2.01022734]\n",
      " [ 1.27492845  0.49895357]\n",
      " [-0.87712469  1.78633493]\n",
      " [ 2.05749323  0.86277873]\n",
      " [-1.26840708 -1.57205123]\n",
      " [-0.4858423   0.06516202]\n",
      " [ 0.98146666 -1.32017227]\n",
      " [-2.05097186  0.30304771]\n",
      " [-0.29020111 -0.59252194]\n",
      " [-0.87712469  0.45697374]\n",
      " [-0.5836629   1.18462408]\n",
      " [-0.87712469  0.49895357]\n",
      " [-0.77930409 -1.57205123]\n",
      " [ 0.00326069  0.96073166]\n",
      " [-0.6814835  -1.25020589]\n",
      " [-1.56186887 -0.49456901]\n",
      " [ 0.68800487  0.90475856]\n",
      " [-0.19238051  0.0791553 ]\n",
      " [ 1.07928726 -1.04030676]\n",
      " [-0.09455991  1.1706308 ]\n",
      " [-0.4858423   0.68086615]\n",
      " [ 0.19890188 -0.34064297]\n",
      " [ 0.78582547 -1.29218572]\n",
      " [ 0.29672248 -0.25668332]\n",
      " [ 1.76403144  1.99623406]\n",
      " [ 0.00326069 -0.21470349]\n",
      " [ 0.10108128  0.02318219]\n",
      " [-0.6814835  -1.48809158]\n",
      " [-0.3880217   0.21908805]\n",
      " [-0.19238051 -0.25668332]]\n"
     ]
    }
   ],
   "source": [
    "print(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "e87fcaeb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\n"
     ]
    }
   ],
   "source": [
    "print(classifier.predict(sc.transform([[49, 74000]])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "628d44a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 1]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 1]\n",
      " [1 1]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [1 1]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [1 1]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [0 0]\n",
      " [1 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 1]\n",
      " [0 0]\n",
      " [0 1]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 1]\n",
      " [0 1]\n",
      " [0 1]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [1 1]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [1 1]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [0 1]\n",
      " [1 0]\n",
      " [0 1]\n",
      " [1 1]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [1 0]\n",
      " [1 1]\n",
      " [1 1]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [1 1]\n",
      " [0 0]\n",
      " [0 1]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [1 1]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 1]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [1 0]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [0 0]]\n"
     ]
    }
   ],
   "source": [
    "y_pred = classifier.predict(X_test)\n",
    "print(np.concatenate((y_pred.reshape(len(y_pred),1), y_test.reshape(len(y_test),1)),1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "231a6375",
   "metadata": {},
   "source": [
    "### The model was trained using a training and test set and the outcome of the confusion matrix is as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "d3ab7e18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[61  4]\n",
      " [10 25]]\n",
      "Accuracy: 0.860\n"
     ]
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "sns.heatmap(cm, annot=True, fmt='g')\n",
    "plt.xlabel('Predicted labels')\n",
    "plt.ylabel('True labels')\n",
    "plt.show()\n",
    "\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(cm)\n",
    "print(f'Accuracy: {accuracy:.3f}')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94426501",
   "metadata": {},
   "source": [
    "### The result shows 4 false positives, 10 false negatives and a accuracy of 86%, which is a good score, since this problem doesn't require a very high precision. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e0f80f6",
   "metadata": {},
   "source": [
    "### Random Forest Classification\n",
    "\n",
    "The colored background shows the predicted class for each point in the graph. The dots in the foreground represent the training data points and their color shows their true class. The line in the middle divides the graph into two parts for each class. The picture gives us an idea about how good the tool is at predicting data and how it might work on new data. The legend tells us which color represents which class.\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "e069312c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.colors import ListedColormap\n",
    "X_set, y_set = sc.inverse_transform(X_train), y_train\n",
    "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 10, stop = X_set[:, 0].max() + 10, step = 0.25),\n",
    "                     np.arange(start = X_set[:, 1].min() - 1000, stop = X_set[:, 1].max() + 1000, step = 0.25))\n",
    "plt.contourf(X1, X2, classifier.predict(sc.transform(np.array([X1.ravel(), X2.ravel()]).T)).reshape(X1.shape),\n",
    "             alpha = 0.75, cmap = ListedColormap(('slategrey', 'slateblue')))\n",
    "plt.xlim(X1.min(), X1.max())\n",
    "plt.ylim(X2.min(), X2.max())\n",
    "for i, j in enumerate(np.unique(y_set)):\n",
    "    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1], color = ListedColormap(('slategrey', 'slateblue'))(i), label = j)\n",
    "plt.title('Random Forest Classification (Train set)')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Estimated Salary')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b65fa95d",
   "metadata": {},
   "source": [
    "This plot shows how well the Random Forest model can tell the difference between the two classes in the test data. It provides a visual representation of the decision boundary that separates the two classes in the feature space. The dots represent the test data points, and their color indicates their true class label. The plot helps us understand how good the model is at correctly predicting the class labels, and where it might make mistakes. The legend tells us which color represents each class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "b7d3201b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.colors import ListedColormap\n",
    "X_set, y_set = sc.inverse_transform(X_test), y_test\n",
    "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 10, stop = X_set[:, 0].max() + 10, step = 0.25),\n",
    "                     np.arange(start = X_set[:, 1].min() - 1000, stop = X_set[:, 1].max() + 1000, step = 0.25))\n",
    "plt.contourf(X1, X2, classifier.predict(sc.transform(np.array([X1.ravel(), X2.ravel()]).T)).reshape(X1.shape),\n",
    "             alpha = 0.75, cmap = ListedColormap(('slategrey', 'slateblue')))\n",
    "plt.xlim(X1.min(), X1.max())\n",
    "plt.ylim(X2.min(), X2.max())\n",
    "for i, j in enumerate(np.unique(y_set)):\n",
    "    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1], color = ListedColormap(('slategrey', 'slateblue'))(i), label = j)\n",
    "plt.title('Random Forest Classification (Test set)')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Estimated Salary')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f1afa40",
   "metadata": {},
   "source": [
    "### Probability\n",
    "We can now use the trained model to predict whether or not someone will purchase a car based on their age and annual salary. The model is used to predict if 43 year old customer X with an annual salary of 43000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "d24765ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\n"
     ]
    }
   ],
   "source": [
    "# create a new data point to predict\n",
    "new_data = [[35, 50000]]\n",
    "\n",
    "# scale the new data using the same scaler used on the training data\n",
    "new_data_scaled = sc.transform(new_data)\n",
    "\n",
    "# make a prediction using the trained classifier\n",
    "prediction = classifier.predict(new_data_scaled)\n",
    "\n",
    "print(prediction)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "264dd997",
   "metadata": {},
   "source": [
    "The outcome \"0\" indicates that it is predicted that this customer X will not buy the car."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04088517",
   "metadata": {},
   "source": [
    "New prediction for 6 (potential) new customers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "b47463e2",
   "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>Age</th>\n",
       "      <th>Annual Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35</td>\n",
       "      <td>50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>42</td>\n",
       "      <td>15700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>51</td>\n",
       "      <td>65000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>23</td>\n",
       "      <td>45600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>36</td>\n",
       "      <td>42000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>41</td>\n",
       "      <td>72000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Annual Salary\n",
       "1   35          50000\n",
       "2   42          15700\n",
       "3   51          65000\n",
       "4   23          45600\n",
       "5   36          42000\n",
       "6   41          72000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_data_2 = [[35, 50000], [42, 15700], [51, 65000], [23, 45600], [36, 42000], [41, 72000]]\n",
    "df = pd.DataFrame(new_data_2, columns=[ 'Age', 'Annual Salary'])\n",
    "df.index = df.index + 1\n",
    "display(df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "053c871e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Customer    Age    Annual Salary  Prediction\n",
      "----------  -----  ---------------  ------------\n",
      "         1     35            50000  No\n",
      "         2     42            15700  No\n",
      "         3     51            65000  Yes\n",
      "         4     23            45600  No\n",
      "         5     36            42000  No\n",
      "         6     41            72000  No\n"
     ]
    }
   ],
   "source": [
    "from tabulate import tabulate\n",
    "\n",
    "results = []\n",
    "\n",
    "for i, data in enumerate(new_data_2):\n",
    "    # scale the new data using the same scaler used on the training data\n",
    "    data_scaled = sc.transform([data])\n",
    "    \n",
    "    # make a prediction using the trained classifier\n",
    "    prediction = classifier.predict(data_scaled)\n",
    "    \n",
    "    # add the results to the table\n",
    "    if prediction == 1:\n",
    "        prediction_text = \"Yes\"\n",
    "    else:\n",
    "        prediction_text = \"No\"\n",
    "    results.append([i+1, data[0], data[1], prediction_text])\n",
    "\n",
    "# print the table\n",
    "headers = [\"Customer\", \"Age\", \"Annual Salary\", \"Prediction\"]\n",
    "print(tabulate(results, headers=headers))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af66ee0d",
   "metadata": {},
   "source": [
    "This table shows that only customer 3 is expected to buy a car."
   ]
  }
 ],
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