notebooks/data_preprocessing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Preprocessing and Exploratory Data Analysis (EDA)\n",
    "\n",
    "This notebook covers the initial data loading, exploratory data analysis, and preprocessing steps for the Wisconsin Breast Cancer dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import necessary libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import sys\n",
    "import os\n",
    "\n",
    "# Get the current notebook's directory\n",
    "current_dir = os.path.dirname(os.path.abspath(\"__file__\"))\n",
    "\n",
    "# Construct the path to the scripts directory\n",
    "utils_dir = os.path.join(current_dir, '..', 'scripts')\n",
    "\n",
    "# Add the utils directory to the system path\n",
    "sys.path.append(utils_dir)\n",
    "\n",
    "#Load functions from the script preprocess.py\n",
    "from preprocess import clean_data, encode_categorical, preprocess_data\n",
    "\n",
    "# Set plot style\n",
    "sns.set(style='whitegrid')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
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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>id</th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>texture_worst</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>concave points_worst</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "      <th>Unnamed: 32</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>842302</td>\n",
       "      <td>M</td>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>...</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>842517</td>\n",
       "      <td>M</td>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>...</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>84300903</td>\n",
       "      <td>M</td>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>...</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84348301</td>\n",
       "      <td>M</td>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>...</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84358402</td>\n",
       "      <td>M</td>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>...</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0    842302         M        17.99         10.38          122.80     1001.0   \n",
       "1    842517         M        20.57         17.77          132.90     1326.0   \n",
       "2  84300903         M        19.69         21.25          130.00     1203.0   \n",
       "3  84348301         M        11.42         20.38           77.58      386.1   \n",
       "4  84358402         M        20.29         14.34          135.10     1297.0   \n",
       "\n",
       "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "0          0.11840           0.27760          0.3001              0.14710   \n",
       "1          0.08474           0.07864          0.0869              0.07017   \n",
       "2          0.10960           0.15990          0.1974              0.12790   \n",
       "3          0.14250           0.28390          0.2414              0.10520   \n",
       "4          0.10030           0.13280          0.1980              0.10430   \n",
       "\n",
       "   ...  texture_worst  perimeter_worst  area_worst  smoothness_worst  \\\n",
       "0  ...          17.33           184.60      2019.0            0.1622   \n",
       "1  ...          23.41           158.80      1956.0            0.1238   \n",
       "2  ...          25.53           152.50      1709.0            0.1444   \n",
       "3  ...          26.50            98.87       567.7            0.2098   \n",
       "4  ...          16.67           152.20      1575.0            0.1374   \n",
       "\n",
       "   compactness_worst  concavity_worst  concave points_worst  symmetry_worst  \\\n",
       "0             0.6656           0.7119                0.2654          0.4601   \n",
       "1             0.1866           0.2416                0.1860          0.2750   \n",
       "2             0.4245           0.4504                0.2430          0.3613   \n",
       "3             0.8663           0.6869                0.2575          0.6638   \n",
       "4             0.2050           0.4000                0.1625          0.2364   \n",
       "\n",
       "   fractal_dimension_worst  Unnamed: 32  \n",
       "0                  0.11890          NaN  \n",
       "1                  0.08902          NaN  \n",
       "2                  0.08758          NaN  \n",
       "3                  0.17300          NaN  \n",
       "4                  0.07678          NaN  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the dataset\n",
    "data = pd.read_csv('../data/breast_cancer_wisconsin.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis (EDA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 569 entries, 0 to 568\n",
      "Data columns (total 33 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   id                       569 non-null    int64  \n",
      " 1   diagnosis                569 non-null    object \n",
      " 2   radius_mean              569 non-null    float64\n",
      " 3   texture_mean             569 non-null    float64\n",
      " 4   perimeter_mean           569 non-null    float64\n",
      " 5   area_mean                569 non-null    float64\n",
      " 6   smoothness_mean          569 non-null    float64\n",
      " 7   compactness_mean         569 non-null    float64\n",
      " 8   concavity_mean           569 non-null    float64\n",
      " 9   concave points_mean      569 non-null    float64\n",
      " 10  symmetry_mean            569 non-null    float64\n",
      " 11  fractal_dimension_mean   569 non-null    float64\n",
      " 12  radius_se                569 non-null    float64\n",
      " 13  texture_se               569 non-null    float64\n",
      " 14  perimeter_se             569 non-null    float64\n",
      " 15  area_se                  569 non-null    float64\n",
      " 16  smoothness_se            569 non-null    float64\n",
      " 17  compactness_se           569 non-null    float64\n",
      " 18  concavity_se             569 non-null    float64\n",
      " 19  concave points_se        569 non-null    float64\n",
      " 20  symmetry_se              569 non-null    float64\n",
      " 21  fractal_dimension_se     569 non-null    float64\n",
      " 22  radius_worst             569 non-null    float64\n",
      " 23  texture_worst            569 non-null    float64\n",
      " 24  perimeter_worst          569 non-null    float64\n",
      " 25  area_worst               569 non-null    float64\n",
      " 26  smoothness_worst         569 non-null    float64\n",
      " 27  compactness_worst        569 non-null    float64\n",
      " 28  concavity_worst          569 non-null    float64\n",
      " 29  concave points_worst     569 non-null    float64\n",
      " 30  symmetry_worst           569 non-null    float64\n",
      " 31  fractal_dimension_worst  569 non-null    float64\n",
      " 32  Unnamed: 32              0 non-null      float64\n",
      "dtypes: float64(31), int64(1), object(1)\n",
      "memory usage: 146.8+ KB\n"
     ]
    }
   ],
   "source": [
    "# Display basic information about the dataset\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>symmetry_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>radius_worst</th>\n",
       "      <th>texture_worst</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>concave points_worst</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.372583</td>\n",
       "      <td>14.127292</td>\n",
       "      <td>19.289649</td>\n",
       "      <td>91.969033</td>\n",
       "      <td>654.889104</td>\n",
       "      <td>0.096360</td>\n",
       "      <td>0.104341</td>\n",
       "      <td>0.088799</td>\n",
       "      <td>0.048919</td>\n",
       "      <td>0.181162</td>\n",
       "      <td>...</td>\n",
       "      <td>16.269190</td>\n",
       "      <td>25.677223</td>\n",
       "      <td>107.261213</td>\n",
       "      <td>880.583128</td>\n",
       "      <td>0.132369</td>\n",
       "      <td>0.254265</td>\n",
       "      <td>0.272188</td>\n",
       "      <td>0.114606</td>\n",
       "      <td>0.290076</td>\n",
       "      <td>0.083946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.483918</td>\n",
       "      <td>3.524049</td>\n",
       "      <td>4.301036</td>\n",
       "      <td>24.298981</td>\n",
       "      <td>351.914129</td>\n",
       "      <td>0.014064</td>\n",
       "      <td>0.052813</td>\n",
       "      <td>0.079720</td>\n",
       "      <td>0.038803</td>\n",
       "      <td>0.027414</td>\n",
       "      <td>...</td>\n",
       "      <td>4.833242</td>\n",
       "      <td>6.146258</td>\n",
       "      <td>33.602542</td>\n",
       "      <td>569.356993</td>\n",
       "      <td>0.022832</td>\n",
       "      <td>0.157336</td>\n",
       "      <td>0.208624</td>\n",
       "      <td>0.065732</td>\n",
       "      <td>0.061867</td>\n",
       "      <td>0.018061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.981000</td>\n",
       "      <td>9.710000</td>\n",
       "      <td>43.790000</td>\n",
       "      <td>143.500000</td>\n",
       "      <td>0.052630</td>\n",
       "      <td>0.019380</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.106000</td>\n",
       "      <td>...</td>\n",
       "      <td>7.930000</td>\n",
       "      <td>12.020000</td>\n",
       "      <td>50.410000</td>\n",
       "      <td>185.200000</td>\n",
       "      <td>0.071170</td>\n",
       "      <td>0.027290</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.156500</td>\n",
       "      <td>0.055040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.700000</td>\n",
       "      <td>16.170000</td>\n",
       "      <td>75.170000</td>\n",
       "      <td>420.300000</td>\n",
       "      <td>0.086370</td>\n",
       "      <td>0.064920</td>\n",
       "      <td>0.029560</td>\n",
       "      <td>0.020310</td>\n",
       "      <td>0.161900</td>\n",
       "      <td>...</td>\n",
       "      <td>13.010000</td>\n",
       "      <td>21.080000</td>\n",
       "      <td>84.110000</td>\n",
       "      <td>515.300000</td>\n",
       "      <td>0.116600</td>\n",
       "      <td>0.147200</td>\n",
       "      <td>0.114500</td>\n",
       "      <td>0.064930</td>\n",
       "      <td>0.250400</td>\n",
       "      <td>0.071460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>13.370000</td>\n",
       "      <td>18.840000</td>\n",
       "      <td>86.240000</td>\n",
       "      <td>551.100000</td>\n",
       "      <td>0.095870</td>\n",
       "      <td>0.092630</td>\n",
       "      <td>0.061540</td>\n",
       "      <td>0.033500</td>\n",
       "      <td>0.179200</td>\n",
       "      <td>...</td>\n",
       "      <td>14.970000</td>\n",
       "      <td>25.410000</td>\n",
       "      <td>97.660000</td>\n",
       "      <td>686.500000</td>\n",
       "      <td>0.131300</td>\n",
       "      <td>0.211900</td>\n",
       "      <td>0.226700</td>\n",
       "      <td>0.099930</td>\n",
       "      <td>0.282200</td>\n",
       "      <td>0.080040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>15.780000</td>\n",
       "      <td>21.800000</td>\n",
       "      <td>104.100000</td>\n",
       "      <td>782.700000</td>\n",
       "      <td>0.105300</td>\n",
       "      <td>0.130400</td>\n",
       "      <td>0.130700</td>\n",
       "      <td>0.074000</td>\n",
       "      <td>0.195700</td>\n",
       "      <td>...</td>\n",
       "      <td>18.790000</td>\n",
       "      <td>29.720000</td>\n",
       "      <td>125.400000</td>\n",
       "      <td>1084.000000</td>\n",
       "      <td>0.146000</td>\n",
       "      <td>0.339100</td>\n",
       "      <td>0.382900</td>\n",
       "      <td>0.161400</td>\n",
       "      <td>0.317900</td>\n",
       "      <td>0.092080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>28.110000</td>\n",
       "      <td>39.280000</td>\n",
       "      <td>188.500000</td>\n",
       "      <td>2501.000000</td>\n",
       "      <td>0.163400</td>\n",
       "      <td>0.345400</td>\n",
       "      <td>0.426800</td>\n",
       "      <td>0.201200</td>\n",
       "      <td>0.304000</td>\n",
       "      <td>...</td>\n",
       "      <td>36.040000</td>\n",
       "      <td>49.540000</td>\n",
       "      <td>251.200000</td>\n",
       "      <td>4254.000000</td>\n",
       "      <td>0.222600</td>\n",
       "      <td>1.058000</td>\n",
       "      <td>1.252000</td>\n",
       "      <td>0.291000</td>\n",
       "      <td>0.663800</td>\n",
       "      <td>0.207500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        diagnosis  radius_mean  texture_mean  perimeter_mean    area_mean  \\\n",
       "count  569.000000   569.000000    569.000000      569.000000   569.000000   \n",
       "mean     0.372583    14.127292     19.289649       91.969033   654.889104   \n",
       "std      0.483918     3.524049      4.301036       24.298981   351.914129   \n",
       "min      0.000000     6.981000      9.710000       43.790000   143.500000   \n",
       "25%      0.000000    11.700000     16.170000       75.170000   420.300000   \n",
       "50%      0.000000    13.370000     18.840000       86.240000   551.100000   \n",
       "75%      1.000000    15.780000     21.800000      104.100000   782.700000   \n",
       "max      1.000000    28.110000     39.280000      188.500000  2501.000000   \n",
       "\n",
       "       smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "count       569.000000        569.000000      569.000000           569.000000   \n",
       "mean          0.096360          0.104341        0.088799             0.048919   \n",
       "std           0.014064          0.052813        0.079720             0.038803   \n",
       "min           0.052630          0.019380        0.000000             0.000000   \n",
       "25%           0.086370          0.064920        0.029560             0.020310   \n",
       "50%           0.095870          0.092630        0.061540             0.033500   \n",
       "75%           0.105300          0.130400        0.130700             0.074000   \n",
       "max           0.163400          0.345400        0.426800             0.201200   \n",
       "\n",
       "       symmetry_mean  ...  radius_worst  texture_worst  perimeter_worst  \\\n",
       "count     569.000000  ...    569.000000     569.000000       569.000000   \n",
       "mean        0.181162  ...     16.269190      25.677223       107.261213   \n",
       "std         0.027414  ...      4.833242       6.146258        33.602542   \n",
       "min         0.106000  ...      7.930000      12.020000        50.410000   \n",
       "25%         0.161900  ...     13.010000      21.080000        84.110000   \n",
       "50%         0.179200  ...     14.970000      25.410000        97.660000   \n",
       "75%         0.195700  ...     18.790000      29.720000       125.400000   \n",
       "max         0.304000  ...     36.040000      49.540000       251.200000   \n",
       "\n",
       "        area_worst  smoothness_worst  compactness_worst  concavity_worst  \\\n",
       "count   569.000000        569.000000         569.000000       569.000000   \n",
       "mean    880.583128          0.132369           0.254265         0.272188   \n",
       "std     569.356993          0.022832           0.157336         0.208624   \n",
       "min     185.200000          0.071170           0.027290         0.000000   \n",
       "25%     515.300000          0.116600           0.147200         0.114500   \n",
       "50%     686.500000          0.131300           0.211900         0.226700   \n",
       "75%    1084.000000          0.146000           0.339100         0.382900   \n",
       "max    4254.000000          0.222600           1.058000         1.252000   \n",
       "\n",
       "       concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
       "count            569.000000      569.000000               569.000000  \n",
       "mean               0.114606        0.290076                 0.083946  \n",
       "std                0.065732        0.061867                 0.018061  \n",
       "min                0.000000        0.156500                 0.055040  \n",
       "25%                0.064930        0.250400                 0.071460  \n",
       "50%                0.099930        0.282200                 0.080040  \n",
       "75%                0.161400        0.317900                 0.092080  \n",
       "max                0.291000        0.663800                 0.207500  \n",
       "\n",
       "[8 rows x 31 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summary statistics\n",
    "data = clean_data(data)\n",
    "data, label_encoder = encode_categorical(data)\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "diagnosis                  0\n",
       "radius_mean                0\n",
       "texture_mean               0\n",
       "perimeter_mean             0\n",
       "area_mean                  0\n",
       "smoothness_mean            0\n",
       "compactness_mean           0\n",
       "concavity_mean             0\n",
       "concave points_mean        0\n",
       "symmetry_mean              0\n",
       "fractal_dimension_mean     0\n",
       "radius_se                  0\n",
       "texture_se                 0\n",
       "perimeter_se               0\n",
       "area_se                    0\n",
       "smoothness_se              0\n",
       "compactness_se             0\n",
       "concavity_se               0\n",
       "concave points_se          0\n",
       "symmetry_se                0\n",
       "fractal_dimension_se       0\n",
       "radius_worst               0\n",
       "texture_worst              0\n",
       "perimeter_worst            0\n",
       "area_worst                 0\n",
       "smoothness_worst           0\n",
       "compactness_worst          0\n",
       "concavity_worst            0\n",
       "concave points_worst       0\n",
       "symmetry_worst             0\n",
       "fractal_dimension_worst    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for missing values\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Correlation heatmap\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.heatmap(data.corr(), annot=False, cmap='coolwarm', fmt='.6f')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Carlos\\AppData\\Local\\anaconda3\\Lib\\site-packages\\seaborn\\categorical.py:641: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  grouped_vals = vals.groupby(grouper)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the distribution of the target variable\n",
    "plt.figure(figsize=(8, 6))\n",
    "ax = sns.barplot(x=data['diagnosis'].value_counts().index, y = data['diagnosis'].value_counts().values)\n",
    "plt.title('Distribution of Diagnosis', fontsize=15)\n",
    "plt.xlabel('Diagnosis (0 = Benign, 1 = Malignant)', fontsize=12)\n",
    "plt.ylabel('Count', fontsize=12)\n",
    "\n",
    "# Add count labels on top of the bars\n",
    "for p in ax.patches:\n",
    "    ax.annotate(f'{int(p.get_height())}', \n",
    "                (p.get_x() + p.get_width() / 2., p.get_height()), \n",
    "                ha='center', va='center', xytext=(0, 10), \n",
    "                textcoords='offset points', fontsize=12)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Preprocess the data cleaning the data, encoding categorical variables, scaling features, and splitting the data into training and testing sets.\n",
    "X_train, X_test, y_train, y_test = preprocess_data('../data/ai4i2020.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save the Preprocessed Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the preprocessed data\n",
    "np.savez('../data/preprocessed_data.npz', X_train=X_train, X_test=X_test, y_train=y_train, y_test=y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "In this notebook, we have loaded the dataset, performed exploratory data analysis, visualized the data, and preprocessed the data for machine learning. The preprocessed data has been saved and is ready for model training."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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