mlc.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here I'm going to basically try to predict the missing values in the \"Market Category\" column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from numpy import mean, std\n",
    "from main import MultiLabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Engine Fuel Type'].fillna(method='ffill', inplace=True)\n",
    "df['Engine HP'].fillna(df['Engine HP'].median(), inplace = True)\n",
    "df['Engine Cylinders'].fillna(df['Engine Cylinders'].mean(), inplace = True)\n",
    "df['Number of Doors'].fillna(method='ffill', inplace = True)\n",
    "df.drop('Model', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_88861/3728987043.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  prediction_df.drop('Market Category', axis=1, inplace=True)\n"
     ]
    }
   ],
   "source": [
    "#create new df containg rows with missing market category (test_df)\n",
    "prediction_df = df.loc[df['Market Category'].isna()]\n",
    "#drop rows with missing market category\n",
    "df.drop(df.index[df['Market Category'].isna()], inplace=True)\n",
    "#drop market category column\n",
    "prediction_df.drop('Market Category', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_y = df.pop('Market Category')\n",
    "encoder = MultiLabelEncoder(multilabel_column=df_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "encoder.extract_unique_labels()\n",
    "encoder.multilabel_oh_encode()\n",
    "df_y_encoded = encoder.encodings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['y_axis'] = df_y_encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('processed_data.csv', index=False)"
   ]
  }
 ],
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