{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the dataset\n",
    "dataset = pd.read_csv('Data.csv')\n",
    "X = dataset.iloc[:, :-1].values\n",
    "y = dataset.iloc[:, 3].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Taking care of missing data\n",
    "from sklearn.impute import SimpleImputer\n",
    "imputer = SimpleImputer(missing_values = np.nan, strategy = 'mean')\n",
    "imputer.fit(X[:, 1:3])\n",
    "X[:, 1:3] = imputer.transform(X[:, 1:3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Encoding categorical data\n",
    "from sklearn.preprocessing import OneHotEncoder, LabelEncode\n",
    "from sklearn.compose import make_column_transformer\n",
    "preprocess = make_column_transformer(\n",
    "        (OneHotEncoder(), [0]),\n",
    "        remainder = \"passthrough\"\n",
    ")\n",
    "X = preprocess.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "labelencoder_y = LabelEncoder()\n",
    "y = labelencoder_y.fit_transform(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Encoding categorical data\n",
    "# Encoding the Independent Variable\n",
    "from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n",
    "labelencoder_X = LabelEncoder()\n",
    "X[:, 0] = labelencoder_X.fit_transform(X[:, 0])\n",
    "onehotencoder = OneHotEncoder(categories = [0])\n",
    "X = onehotencoder.fit_transform(X).toarray()\n",
    "# Encoding the Dependent Variable\n",
    "labelencoder_y = LabelEncoder()\n",
    "y = labelencoder_y.fit_transform(y)"
   ]
  }
 ],
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