ML Practical-03.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6a8a797c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Given a bank customer, build a neural network-based classifier that can determine whether \n",
    "# they will leave or not in the next 6 months.\n",
    "# Dataset Description: The case study is from an open-source dataset from Kaggle.\n",
    "# The dataset contains 10,000 sample points with 14 distinct features such as\n",
    "# CustomerId, CreditScore, Geography, Gender, Age, Tenure, Balance, etc.\n",
    "# Link to the Kaggle project:\n",
    "# https://www.kaggle.com/barelydedicated/bank-customer-churn-modeling\n",
    "# Perform following steps:\n",
    "# 1. Read the dataset.\n",
    "# 2. Distinguish the feature and target set and divide the data set into training and test sets.\n",
    "# 3. Normalize the train and test data. \n",
    "# 4. Initialize and build the model. Identify the points of improvement and implement the same. \n",
    "# 5. Print the accuracy score and confusion matrix (5 points)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a2dfd600",
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'tensorflow'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_31424\\1090667521.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmetrics\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0maccuracy_score\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mconfusion_matrix\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mkeras\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      7\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[1;31m# 1. Read the dataset\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'tensorflow'"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "\n",
    "# 1. Read the dataset\n",
    "data = pd.read_csv(\"Churn_Modelling.csv\")  # Replace with the actual path to the dataset\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5d73b009",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2. Distinguish features and target\n",
    "X = data.drop(\"Exited\", axis=1)  # Features\n",
    "y = data[\"Exited\"]  # Target variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b9572ded",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RowNumber</th>\n",
       "      <th>CustomerId</th>\n",
       "      <th>Surname</th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>15634602</td>\n",
       "      <td>Hargrave</td>\n",
       "      <td>619</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101348.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>15647311</td>\n",
       "      <td>Hill</td>\n",
       "      <td>608</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>83807.86</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>112542.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>15619304</td>\n",
       "      <td>Onio</td>\n",
       "      <td>502</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>8</td>\n",
       "      <td>159660.80</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113931.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>15701354</td>\n",
       "      <td>Boni</td>\n",
       "      <td>699</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>93826.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>15737888</td>\n",
       "      <td>Mitchell</td>\n",
       "      <td>850</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>125510.82</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>79084.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9995</th>\n",
       "      <td>9996</td>\n",
       "      <td>15606229</td>\n",
       "      <td>Obijiaku</td>\n",
       "      <td>771</td>\n",
       "      <td>France</td>\n",
       "      <td>Male</td>\n",
       "      <td>39</td>\n",
       "      <td>5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>96270.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>9997</td>\n",
       "      <td>15569892</td>\n",
       "      <td>Johnstone</td>\n",
       "      <td>516</td>\n",
       "      <td>France</td>\n",
       "      <td>Male</td>\n",
       "      <td>35</td>\n",
       "      <td>10</td>\n",
       "      <td>57369.61</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101699.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>9998</td>\n",
       "      <td>15584532</td>\n",
       "      <td>Liu</td>\n",
       "      <td>709</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>36</td>\n",
       "      <td>7</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>42085.58</td>\n",
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       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>9999</td>\n",
       "      <td>15682355</td>\n",
       "      <td>Sabbatini</td>\n",
       "      <td>772</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Male</td>\n",
       "      <td>42</td>\n",
       "      <td>3</td>\n",
       "      <td>75075.31</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>92888.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>10000</td>\n",
       "      <td>15628319</td>\n",
       "      <td>Walker</td>\n",
       "      <td>792</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>28</td>\n",
       "      <td>4</td>\n",
       "      <td>130142.79</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>38190.78</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      RowNumber  CustomerId    Surname  CreditScore Geography  Gender  Age  \\\n",
       "0             1    15634602   Hargrave          619    France  Female   42   \n",
       "1             2    15647311       Hill          608     Spain  Female   41   \n",
       "2             3    15619304       Onio          502    France  Female   42   \n",
       "3             4    15701354       Boni          699    France  Female   39   \n",
       "4             5    15737888   Mitchell          850     Spain  Female   43   \n",
       "...         ...         ...        ...          ...       ...     ...  ...   \n",
       "9995       9996    15606229   Obijiaku          771    France    Male   39   \n",
       "9996       9997    15569892  Johnstone          516    France    Male   35   \n",
       "9997       9998    15584532        Liu          709    France  Female   36   \n",
       "9998       9999    15682355  Sabbatini          772   Germany    Male   42   \n",
       "9999      10000    15628319     Walker          792    France  Female   28   \n",
       "\n",
       "      Tenure    Balance  NumOfProducts  HasCrCard  IsActiveMember  \\\n",
       "0          2       0.00              1          1               1   \n",
       "1          1   83807.86              1          0               1   \n",
       "2          8  159660.80              3          1               0   \n",
       "3          1       0.00              2          0               0   \n",
       "4          2  125510.82              1          1               1   \n",
       "...      ...        ...            ...        ...             ...   \n",
       "9995       5       0.00              2          1               0   \n",
       "9996      10   57369.61              1          1               1   \n",
       "9997       7       0.00              1          0               1   \n",
       "9998       3   75075.31              2          1               0   \n",
       "9999       4  130142.79              1          1               0   \n",
       "\n",
       "      EstimatedSalary  \n",
       "0           101348.88  \n",
       "1           112542.58  \n",
       "2           113931.57  \n",
       "3            93826.63  \n",
       "4            79084.10  \n",
       "...               ...  \n",
       "9995         96270.64  \n",
       "9996        101699.77  \n",
       "9997         42085.58  \n",
       "9998         92888.52  \n",
       "9999         38190.78  \n",
       "\n",
       "[10000 rows x 13 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c9620386",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2. Divide the dataset into training and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e43de6fd",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: \"P'an\"",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_31424\\4130372370.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# 3. Normalize the data\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mscaler\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mStandardScaler\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mX_train\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mscaler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[0mX_test\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mscaler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\base.py\u001b[0m in \u001b[0;36mfit_transform\u001b[1;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[0;32m    850\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0my\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    851\u001b[0m             \u001b[1;31m# fit method of arity 1 (unsupervised transformation)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 852\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    853\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    854\u001b[0m             \u001b[1;31m# fit method of arity 2 (supervised transformation)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\_data.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m    804\u001b[0m         \u001b[1;31m# Reset internal state before fitting\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    805\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 806\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpartial_fit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    807\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    808\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mpartial_fit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\_data.py\u001b[0m in \u001b[0;36mpartial_fit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m    839\u001b[0m         \"\"\"\n\u001b[0;32m    840\u001b[0m         \u001b[0mfirst_call\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"n_samples_seen_\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 841\u001b[1;33m         X = self._validate_data(\n\u001b[0m\u001b[0;32m    842\u001b[0m             \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    843\u001b[0m             \u001b[0maccept_sparse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"csr\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"csc\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\base.py\u001b[0m in \u001b[0;36m_validate_data\u001b[1;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[0;32m    564\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Validation should be done on X, y or both.\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    565\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mno_val_X\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mno_val_y\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 566\u001b[1;33m             \u001b[0mX\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mcheck_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    567\u001b[0m             \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    568\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mno_val_X\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mno_val_y\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)\u001b[0m\n\u001b[0;32m    744\u001b[0m                     \u001b[0marray\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcasting\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"unsafe\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    745\u001b[0m                 \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 746\u001b[1;33m                     \u001b[0marray\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    747\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mComplexWarning\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mcomplex_warning\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    748\u001b[0m                 raise ValueError(\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m__array__\u001b[1;34m(self, dtype)\u001b[0m\n\u001b[0;32m   2062\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2063\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__array__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mnpt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDTypeLike\u001b[0m \u001b[1;33m|\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2064\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2065\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2066\u001b[0m     def __array_wrap__(\n",
      "\u001b[1;31mValueError\u001b[0m: could not convert string to float: \"P'an\""
     ]
    }
   ],
   "source": [
    "# 3. Normalize the data\n",
    "# scaler = StandardScaler()\n",
    "# X_train = scaler.fit_transform(X_train)\n",
    "# X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1d31a3ea",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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