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Nigerian_Car_Price_Prediction.ipynb

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+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "id": "c0b8d60a",
+      "metadata": {
+        "id": "c0b8d60a"
+      },
+      "outputs": [],
+      "source": [
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "import seaborn as sns\n",
+        "import matplotlib.pyplot as plt\n",
+        "import warnings\n",
+        "warnings.filterwarnings(\"ignore\")\n",
+        "sns.set_style(\"darkgrid\")\n",
+        "sns.set_palette('RdYlGn')\n",
+        "\n",
+        "#model\n",
+        "from sklearn.preprocessing import LabelEncoder,StandardScaler,MinMaxScaler\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.metrics import mean_squared_error, r2_score\n",
+        "from sklearn.ensemble import RandomForestRegressor\n",
+        "from xgboost import XGBRegressor\n",
+        "from sklearn.linear_model import LinearRegression\n",
+        "\n",
+        "import gradio as gr\n",
+        "import joblib"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "id": "11273e4d",
+      "metadata": {
+        "id": "11273e4d"
+      },
+      "outputs": [],
+      "source": [
+        "df = pd.read_csv(\"/content/Nigerian_Car_Prices.csv\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "id": "dffa0dba",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 340
+        },
+        "id": "dffa0dba",
+        "outputId": "eb17a45d-8e91-41b5-ddae-0be82f2fe1f6"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   Unnamed: 0           Make  Year of manufacture      Condition   Mileage  \\\n",
+              "0           0         Toyota               2007.0  Nigerian Used  166418.0   \n",
+              "1           1          Lexus                  NaN            NaN  138024.0   \n",
+              "2           2  Mercedes-Benz               2008.0  Nigerian Used  376807.0   \n",
+              "3           3          Lexus                  NaN            NaN  213362.0   \n",
+              "4           4  Mercedes-Benz                  NaN            NaN  106199.0   \n",
+              "\n",
+              "   Engine Size    Fuel Transmission      Price Build  \n",
+              "0       2400.0  Petrol    Automatic  3,120,000   NaN  \n",
+              "1          NaN     NaN    Automatic  5,834,000   NaN  \n",
+              "2       3000.0  Petrol    Automatic  3,640,000   NaN  \n",
+              "3          NaN     NaN    Automatic  3,594,000   NaN  \n",
+              "4          NaN     NaN    Automatic  8,410,000   NaN  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-e7f12378-3a0c-4bd1-b3da-c57aedf9443c\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <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>Unnamed: 0</th>\n",
+              "      <th>Make</th>\n",
+              "      <th>Year of manufacture</th>\n",
+              "      <th>Condition</th>\n",
+              "      <th>Mileage</th>\n",
+              "      <th>Engine Size</th>\n",
+              "      <th>Fuel</th>\n",
+              "      <th>Transmission</th>\n",
+              "      <th>Price</th>\n",
+              "      <th>Build</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>0</td>\n",
+              "      <td>Toyota</td>\n",
+              "      <td>2007.0</td>\n",
+              "      <td>Nigerian Used</td>\n",
+              "      <td>166418.0</td>\n",
+              "      <td>2400.0</td>\n",
+              "      <td>Petrol</td>\n",
+              "      <td>Automatic</td>\n",
+              "      <td>3,120,000</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1</td>\n",
+              "      <td>Lexus</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>138024.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Automatic</td>\n",
+              "      <td>5,834,000</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2</td>\n",
+              "      <td>Mercedes-Benz</td>\n",
+              "      <td>2008.0</td>\n",
+              "      <td>Nigerian Used</td>\n",
+              "      <td>376807.0</td>\n",
+              "      <td>3000.0</td>\n",
+              "      <td>Petrol</td>\n",
+              "      <td>Automatic</td>\n",
+              "      <td>3,640,000</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>3</td>\n",
+              "      <td>Lexus</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>213362.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Automatic</td>\n",
+              "      <td>3,594,000</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>4</td>\n",
+              "      <td>Mercedes-Benz</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>106199.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Automatic</td>\n",
+              "      <td>8,410,000</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e7f12378-3a0c-4bd1-b3da-c57aedf9443c')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-e7f12378-3a0c-4bd1-b3da-c57aedf9443c button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-e7f12378-3a0c-4bd1-b3da-c57aedf9443c');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ],
+      "source": [
+        "df.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "id": "30f57450",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "30f57450",
+        "outputId": "462327ca-b494-4cc7-d8d1-aa765e166650"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 4095 entries, 0 to 4094\n",
+            "Data columns (total 10 columns):\n",
+            " #   Column               Non-Null Count  Dtype  \n",
+            "---  ------               --------------  -----  \n",
+            " 0   Unnamed: 0           4095 non-null   int64  \n",
+            " 1   Make                 4095 non-null   object \n",
+            " 2   Year of manufacture  3617 non-null   float64\n",
+            " 3   Condition            3616 non-null   object \n",
+            " 4   Mileage              4024 non-null   float64\n",
+            " 5   Engine Size          3584 non-null   float64\n",
+            " 6   Fuel                 3607 non-null   object \n",
+            " 7   Transmission         4075 non-null   object \n",
+            " 8   Price                4095 non-null   object \n",
+            " 9   Build                1127 non-null   object \n",
+            "dtypes: float64(3), int64(1), object(6)\n",
+            "memory usage: 320.0+ KB\n"
+          ]
+        }
+      ],
+      "source": [
+        "df.info()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "2b138a73",
+      "metadata": {
+        "id": "2b138a73"
+      },
+      "source": [
+        "### Data Cleaning"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "id": "fd78bcc0",
+      "metadata": {
+        "id": "fd78bcc0"
+      },
+      "outputs": [],
+      "source": [
+        "df = df.drop('Build', axis = 1)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "id": "60013f82",
+      "metadata": {
+        "id": "60013f82"
+      },
+      "outputs": [],
+      "source": [
+        "df = df.dropna()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "id": "62b833d4",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "62b833d4",
+        "outputId": "05f88dbc-c2db-45be-c1c1-0f8553706eae"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(3523, 9)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 9
+        }
+      ],
+      "source": [
+        "df.shape"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "id": "e04b4172",
+      "metadata": {
+        "id": "e04b4172"
+      },
+      "outputs": [],
+      "source": [
+        "df['Price'] = df['Price'].str.replace(',', '') \n",
+        "df['Price'] = df['Price'].astype(float)  \n",
+        "\n",
+        "df['Year of manufacture'] = df['Year of manufacture'].astype(int)  "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "id": "c62daca5",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 300
+        },
+        "id": "c62daca5",
+        "outputId": "6639a400-6ded-4f42-cbe5-4469c7fa27f2"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "        Unnamed: 0  Year of manufacture       Mileage    Engine Size  \\\n",
+              "count  3523.000000          3523.000000  3.523000e+03    3523.000000   \n",
+              "mean   2089.276753          2007.921090  1.901794e+05    3170.591541   \n",
+              "std    1187.608368             4.303771  2.215162e+05    4641.379934   \n",
+              "min       0.000000          1992.000000  1.000000e+00       3.000000   \n",
+              "25%    1066.500000          2005.000000  1.070360e+05    2000.000000   \n",
+              "50%    2085.000000          2008.000000  1.670060e+05    2500.000000   \n",
+              "75%    3136.500000          2011.000000  2.397715e+05    3500.000000   \n",
+              "max    4094.000000          2021.000000  9.976050e+06  184421.000000   \n",
+              "\n",
+              "              Price  \n",
+              "count  3.523000e+03  \n",
+              "mean   4.060590e+06  \n",
+              "std    4.520306e+06  \n",
+              "min    4.725000e+05  \n",
+              "25%    1.800000e+06  \n",
+              "50%    2.835000e+06  \n",
+              "75%    4.500000e+06  \n",
+              "max    5.880000e+07  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-c6ed75da-f06e-4c10-914e-eb4a7cf570d1\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <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>Unnamed: 0</th>\n",
+              "      <th>Year of manufacture</th>\n",
+              "      <th>Mileage</th>\n",
+              "      <th>Engine Size</th>\n",
+              "      <th>Price</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>3523.000000</td>\n",
+              "      <td>3523.000000</td>\n",
+              "      <td>3.523000e+03</td>\n",
+              "      <td>3523.000000</td>\n",
+              "      <td>3.523000e+03</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>2089.276753</td>\n",
+              "      <td>2007.921090</td>\n",
+              "      <td>1.901794e+05</td>\n",
+              "      <td>3170.591541</td>\n",
+              "      <td>4.060590e+06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>1187.608368</td>\n",
+              "      <td>4.303771</td>\n",
+              "      <td>2.215162e+05</td>\n",
+              "      <td>4641.379934</td>\n",
+              "      <td>4.520306e+06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>1992.000000</td>\n",
+              "      <td>1.000000e+00</td>\n",
+              "      <td>3.000000</td>\n",
+              "      <td>4.725000e+05</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>1066.500000</td>\n",
+              "      <td>2005.000000</td>\n",
+              "      <td>1.070360e+05</td>\n",
+              "      <td>2000.000000</td>\n",
+              "      <td>1.800000e+06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>2085.000000</td>\n",
+              "      <td>2008.000000</td>\n",
+              "      <td>1.670060e+05</td>\n",
+              "      <td>2500.000000</td>\n",
+              "      <td>2.835000e+06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>3136.500000</td>\n",
+              "      <td>2011.000000</td>\n",
+              "      <td>2.397715e+05</td>\n",
+              "      <td>3500.000000</td>\n",
+              "      <td>4.500000e+06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>4094.000000</td>\n",
+              "      <td>2021.000000</td>\n",
+              "      <td>9.976050e+06</td>\n",
+              "      <td>184421.000000</td>\n",
+              "      <td>5.880000e+07</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c6ed75da-f06e-4c10-914e-eb4a7cf570d1')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-c6ed75da-f06e-4c10-914e-eb4a7cf570d1 button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-c6ed75da-f06e-4c10-914e-eb4a7cf570d1');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 11
+        }
+      ],
+      "source": [
+        "df.describe()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "910be70f",
+      "metadata": {
+        "id": "910be70f"
+      },
+      "source": [
+        "### EDA"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "90e49305",
+      "metadata": {
+        "id": "90e49305"
+      },
+      "source": [
+        "### Feature Engineering"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#the brand new is just 5, it will be drop\n",
+        "# Dropping the 'Brand New' category\n",
+        "df = df[df['Condition'] != 'Brand New']"
+      ],
+      "metadata": {
+        "id": "PkF02_5ah3bB"
+      },
+      "id": "PkF02_5ah3bB",
+      "execution_count": 35,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 38,
+      "id": "544f2b81",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "544f2b81",
+        "outputId": "efdf1889-b1b6-445c-901a-acab17d1cda1"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "['scaler.joblib']"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 38
+        }
+      ],
+      "source": [
+        "X = df.drop(['Unnamed: 0', 'Price'], axis = 1)\n",
+        "y = df.Price\n",
+        "\n",
+        "make_counts = X['Make'].value_counts()\n",
+        "\n",
+        "\n",
+        "# Get the values to replace with 'Others'\n",
+        "make_others = make_counts[make_counts < 14].index.tolist()\n",
+        "\n",
+        "# Replace values with 'Others'\n",
+        "X['Make'] = X['Make'].apply(lambda x: 'Others' if x in make_others else x)\n",
+        "\n",
+        "X_train,X_test, y_train,y_test = train_test_split(X,y, test_size = 0.2, random_state=10)\n",
+        "\n",
+        "\n",
+        "# Initializing the encoders and scaler for each column\n",
+        "make_encoder = LabelEncoder()\n",
+        "fuel_encoder = LabelEncoder()\n",
+        "transmission_encoder = LabelEncoder()\n",
+        "condition_encoder = LabelEncoder()\n",
+        "scaler = MinMaxScaler()\n",
+        "\n",
+        "# Encoding and scaling each column individually\n",
+        "X_train['Make'] = make_encoder.fit_transform(X_train['Make'])\n",
+        "X_test['Make'] = make_encoder.transform(X_test['Make'])\n",
+        "\n",
+        "X_train['Fuel'] = fuel_encoder.fit_transform(X_train['Fuel'])\n",
+        "X_test['Fuel'] = fuel_encoder.transform(X_test['Fuel'])\n",
+        "\n",
+        "X_train['Transmission'] = transmission_encoder.fit_transform(X_train['Transmission'])\n",
+        "X_test['Transmission'] = transmission_encoder.transform(X_test['Transmission'])\n",
+        "\n",
+        "X_train['Condition'] = condition_encoder.fit_transform(X_train['Condition'])\n",
+        "X_test['Condition'] = condition_encoder.transform(X_test['Condition'])\n",
+        "\n",
+        "X_train[['Year of manufacture', 'Mileage', 'Engine Size']] = scaler.fit_transform(X_train[['Year of manufacture', 'Mileage', 'Engine Size']])\n",
+        "X_test[['Year of manufacture', 'Mileage', 'Engine Size']] = scaler.transform(X_test[['Year of manufacture', 'Mileage', 'Engine Size']])\n",
+        "\n",
+        "# Save the encoders and scaler\n",
+        "joblib.dump(make_encoder, \"make_encoder.joblib\",compress=3)\n",
+        "joblib.dump(fuel_encoder, \"fuel_encoder.joblib\",compress=3)\n",
+        "joblib.dump(transmission_encoder, \"transmission_encoder.joblib\",compress=3)\n",
+        "joblib.dump(condition_encoder, \"condition_encoder.joblib\",compress=3)\n",
+        "joblib.dump(scaler, \"scaler.joblib\",compress=3)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "307eab41",
+      "metadata": {
+        "id": "307eab41"
+      },
+      "source": [
+        "#### Needed Model"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 39,
+      "id": "23aaa0f7",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "23aaa0f7",
+        "outputId": "7ac3f946-76f2-4e32-bda3-84106fcec209"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Random Forest RMSE: 1900923.15\n",
+            "XGBoost RMSE: 1881430.11\n",
+            "Linear Regression RMSE: 3227815.24\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Initialize the models\n",
+        "rf_model = RandomForestRegressor(random_state=42)\n",
+        "xgb_model = XGBRegressor(random_state=42)\n",
+        "lr_model = LinearRegression()\n",
+        "\n",
+        "# Fit the models on the training data\n",
+        "rf_model.fit(X_train, y_train)\n",
+        "xgb_model.fit(X_train, y_train)\n",
+        "lr_model.fit(X_train, y_train)\n",
+        "\n",
+        "# Make predictions on the testing data\n",
+        "rf_preds = rf_model.predict(X_test)\n",
+        "xgb_preds = xgb_model.predict(X_test)\n",
+        "lr_preds = lr_model.predict(X_test)\n",
+        "\n",
+        "# Evaluate the models using root mean squared error (RMSE)\n",
+        "rf_rmse = mean_squared_error(y_test, rf_preds, squared=False)\n",
+        "xgb_rmse = mean_squared_error(y_test, xgb_preds, squared=False)\n",
+        "lr_rmse = mean_squared_error(y_test, lr_preds, squared=False)\n",
+        "\n",
+        "# Print the RMSE scores\n",
+        "print(f\"Random Forest RMSE: {rf_rmse:.2f}\")\n",
+        "print(f\"XGBoost RMSE: {xgb_rmse:.2f}\")\n",
+        "print(f\"Linear Regression RMSE: {lr_rmse:.2f}\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#  R2 score\n",
+        "rf_r2 = r2_score(y_test, rf_preds)\n",
+        "print(\"Random Forest R2 Score:\", rf_r2)\n",
+        "\n",
+        "\n",
+        "xgb_r2 = r2_score(y_test, xgb_preds)\n",
+        "print(\"XGBoost R2 Score:\", xgb_r2)\n",
+        "\n",
+        "\n",
+        "lr_r2 = r2_score(y_test, lr_preds)\n",
+        "print(\"Linear Regression R2 Score:\", lr_r2)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "HAij8ecNkQf4",
+        "outputId": "cfeb36b4-201b-413a-8b4f-ce722b9d7ef3"
+      },
+      "id": "HAij8ecNkQf4",
+      "execution_count": 40,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Random Forest R2 Score: 0.7692007346747749\n",
+            "XGBoost R2 Score: 0.7739099336774033\n",
+            "Linear Regression R2 Score: 0.33453895627915986\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 41,
+      "id": "f9dfda36",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "f9dfda36",
+        "outputId": "69882d26-6915-4f06-c5af-d38ce97417cd"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "['car_model.joblib']"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 41
+        }
+      ],
+      "source": [
+        "joblib.dump(xgb_model, \"car_model.joblib\", compress=3)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "faeff4c7",
+      "metadata": {
+        "id": "faeff4c7"
+      },
+      "source": [
+        "**Note: Many Models have been built, but only the needed ones were kept**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 42,
+      "id": "1b6ca9be",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        },
+        "id": "1b6ca9be",
+        "outputId": "a049c64e-ea4f-44d3-9bfb-4a03cc01a7cf"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "sns.histplot(xgb_preds, label='prediction',color='red')\n",
+        "sns.histplot(y_test, label='actual price', color = 'blue')\n",
+        "plt.title('Prediction Vs Actual')\n",
+        "plt.legend()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e921f047",
+      "metadata": {
+        "id": "e921f047"
+      },
+      "source": [
+        "### Prediction"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 43,
+      "id": "e23ac604",
+      "metadata": {
+        "id": "e23ac604"
+      },
+      "outputs": [],
+      "source": [
+        "import joblib\n",
+        "def predict_car_price(make, year, condition, mileage, engine_size, fuel, transmission):\n",
+        "    # Load the encoders and scaler\n",
+        "    make_encoder = joblib.load(\"make_encoder.joblib\")\n",
+        "    fuel_encoder = joblib.load(\"fuel_encoder.joblib\")\n",
+        "    transmission_encoder = joblib.load(\"transmission_encoder.joblib\")\n",
+        "    condition_encoder = joblib.load(\"condition_encoder.joblib\")\n",
+        "    scaler = joblib.load(\"scaler.joblib\")\n",
+        "\n",
+        "    # Preprocess the input\n",
+        "    make_encoded = make_encoder.transform([make])[0]\n",
+        "    numerical_value = scaler.transform([[year,mileage, engine_size]])\n",
+        "    year_scaled = numerical_value[0][0]\n",
+        "    mileage_scaled = numerical_value[0][1]\n",
+        "    engine_size_scaled = numerical_value[0][2]\n",
+        "    fuel_encoded = fuel_encoder.transform([fuel])[0]\n",
+        "    condition_encoded = condition_encoder.transform([condition])[0]\n",
+        "    transmission_encoded = transmission_encoder.transform([transmission])[0]\n",
+        "\n",
+        "    input_data = [[make_encoded, year_scaled, condition_encoded, mileage_scaled, engine_size_scaled, fuel_encoded, transmission_encoded]]\n",
+        "    input_df = pd.DataFrame(input_data, columns=['Make', 'Year of manufacture', 'Condition', 'Mileage', 'Engine Size', 'Fuel', 'Transmission'])\n",
+        "\n",
+        "    # Make predictions\n",
+        "    predicted_price = xgb_model.predict(input_df)\n",
+        "    return round(predicted_price[0], 2)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 44,
+      "id": "07692f2e",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "07692f2e",
+        "outputId": "c70a6f63-72db-4129-e38a-2f319e506f35"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "4970118.0"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 44
+        }
+      ],
+      "source": [
+        "predict_car_price('Toyota', 2010,'Nigerian Used', 3000, 2300, 'Petrol', 'Automatic')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "fce6ae74",
+      "metadata": {
+        "id": "fce6ae74"
+      },
+      "source": [
+        "### Gradio Interface"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import gradio as gr\n",
+        "import joblib\n",
+        "def predict_car_price(make, year, condition, mileage, engine_size, fuel, transmission):\n",
+        "    # Load the encoders and scaler\n",
+        "    make_encoder = joblib.load(\"make_encoder.joblib\")\n",
+        "    fuel_encoder = joblib.load(\"fuel_encoder.joblib\")\n",
+        "    transmission_encoder = joblib.load(\"transmission_encoder.joblib\")\n",
+        "    condition_encoder = joblib.load(\"condition_encoder.joblib\")\n",
+        "    scaler = joblib.load(\"scaler.joblib\")\n",
+        "\n",
+        "    make_encoded = make_encoder.transform([make])[0]\n",
+        "    numerical_value = scaler.transform([[year,mileage, engine_size]])\n",
+        "    year_scaled = numerical_value[0][0]\n",
+        "    mileage_scaled = numerical_value[0][1]\n",
+        "    engine_size_scaled = numerical_value[0][2]\n",
+        "    fuel_encoded = fuel_encoder.transform([fuel])[0]\n",
+        "    condition_encoded = condition_encoder.transform([condition])[0]\n",
+        "    transmission_encoded = transmission_encoder.transform([transmission])[0]\n",
+        "    input_data = [[make_encoded, year_scaled, condition_encoded, mileage_scaled, engine_size_scaled, fuel_encoded, transmission_encoded]]\n",
+        "    input_df = pd.DataFrame(input_data, columns=['Make', 'Year of manufacture', 'Condition', 'Mileage', 'Engine Size', 'Fuel', 'Transmission'])\n",
+        "\n",
+        "    # Make predictions\n",
+        "    predicted_price = xgb_model.predict(input_df)\n",
+        "    return round(predicted_price[0], 2)\n",
+        "make_dropdown = gr.inputs.Dropdown(['Acura', 'Audi', 'BMW', 'Chevrolet', 'Dodge', 'Ford', 'Honda',\n",
+        "       'Hyundai', 'Infiniti', 'Kia', 'Land Rover', 'Lexus', 'Mazda',\n",
+        "       'Mercedes-Benz', 'Mitsubishi', 'Nissan', 'Peugeot',\n",
+        "       'Pontiac', 'Toyota', 'Volkswagen', 'Volvo'], label=\"Make\")\n",
+        "condition_dropdown = gr.inputs.Dropdown(['Foreign Used', 'Nigerian Used'], label=\"Condition\")\n",
+        "fuel_dropdown = gr.inputs.Dropdown([\"Petrol\", \"Diesel\", \"Electric\"], label=\"Fuel\")\n",
+        "transmission_dropdown = gr.inputs.Dropdown([\"Manual\", \"Automatic\", \"AMT\"], label=\"Transmission\")\n",
+        "year_slider = gr.inputs.Slider(minimum=1992, maximum=2021, step=1, default=2010, label=\"Year\")\n",
+        "mileage_slider = gr.inputs.Slider(minimum=1, maximum=300000, step=10, default=80000, label=\"Mileage\")\n",
+        "engine_size_slider = gr.inputs.Slider(minimum=1, maximum=20000, step=1, default=100, label=\"Engine Size\")\n",
+        "\n",
+        "iface = gr.Interface(\n",
+        "fn=predict_car_price,\n",
+        "inputs=[make_dropdown, year_slider, condition_dropdown, mileage_slider, engine_size_slider, fuel_dropdown, transmission_dropdown],\n",
+        "outputs=\"number\",\n",
+        "title=\"Car Price Prediction\",\n",
+        "    description=\"Predict the price of a car based on its details, in Naira.\",\n",
+        "    examples=[\n",
+        "        [\"Toyota\", 2010, \"Nigerian Used\", 80000, 2.0, \"Petrol\", \"Automatic\"],\n",
+        "        [\"Mercedes-Benz\", 2015, \"Foreign Used\", 50000, 1000, \"Diesel\", \"AMT\"],\n",
+        "    ],css=\".gradio-container {background-color: lightgreen}\"\n",
+        ")\n",
+        "\n",
+        "iface.launch(share = True)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 611
+        },
+        "id": "0ZNR9WJ5m5dA",
+        "outputId": "b4292dcc-3397-46db-d5b2-3932ff51c657"
+      },
+      "id": "0ZNR9WJ5m5dA",
+      "execution_count": 46,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
+            "Running on public URL: https://99918e8c858d7db896.gradio.live\n",
+            "\n",
+            "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "<div><iframe src=\"https://99918e8c858d7db896.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": []
+          },
+          "metadata": {},
+          "execution_count": 46
+        }
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.8.8"
+    },
+    "colab": {
+      "provenance": []
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}

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+size 118321

condition_encoder.joblib

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fuel_encoder.joblib

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+oid sha256:d928d31f16f22cd262183f89b49b4c7d6f78757becc7355d1368e7c49914cd19
+size 415

make_encoder.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:521edbe598a7059ded42554fec5ca3aa693e5fa2099a33c5892b7de76958a6a5
+size 576

nigerian_car_price_model.py

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+# -*- coding: utf-8 -*-
+"""Nigerian Car Price Model.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1RtrEB_oX2Q9llgG2KysiBNuIg-EEtpdv
+"""
+
+import pandas as pd
+import numpy as np
+import seaborn as sns
+import matplotlib.pyplot as plt
+import warnings
+warnings.filterwarnings("ignore")
+sns.set_style("darkgrid")
+sns.set_palette('RdYlGn')
+
+#model
+from sklearn.preprocessing import LabelEncoder,StandardScaler,MinMaxScaler
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import mean_squared_error, r2_score
+from sklearn.ensemble import RandomForestRegressor
+from xgboost import XGBRegressor
+from sklearn.linear_model import LinearRegression
+
+import gradio as gr
+import joblib
+
+df = pd.read_csv("/content/Nigerian_Car_Prices.csv")
+
+df.head()
+
+df.info()
+
+"""### Data Cleaning"""
+
+df = df.drop('Build', axis = 1)
+
+df = df.dropna()
+
+df.shape
+
+df['Price'] = df['Price'].str.replace(',', '') 
+df['Price'] = df['Price'].astype(float)  
+
+df['Year of manufacture'] = df['Year of manufacture'].astype(int)
+
+df.describe()
+
+"""### EDA
+
+### Feature Engineering
+"""
+
+#the brand new is just 5, it will be drop
+# Dropping the 'Brand New' category
+df = df[df['Condition'] != 'Brand New']
+
+X = df.drop(['Unnamed: 0', 'Price'], axis = 1)
+y = df.Price
+
+make_counts = X['Make'].value_counts()
+
+
+# Get the values to replace with 'Others'
+make_others = make_counts[make_counts < 14].index.tolist()
+
+# Replace values with 'Others'
+X['Make'] = X['Make'].apply(lambda x: 'Others' if x in make_others else x)
+
+X_train,X_test, y_train,y_test = train_test_split(X,y, test_size = 0.2, random_state=10)
+
+
+# Initializing the encoders and scaler for each column
+make_encoder = LabelEncoder()
+fuel_encoder = LabelEncoder()
+transmission_encoder = LabelEncoder()
+condition_encoder = LabelEncoder()
+scaler = MinMaxScaler()
+
+# Encoding and scaling each column individually
+X_train['Make'] = make_encoder.fit_transform(X_train['Make'])
+X_test['Make'] = make_encoder.transform(X_test['Make'])
+
+X_train['Fuel'] = fuel_encoder.fit_transform(X_train['Fuel'])
+X_test['Fuel'] = fuel_encoder.transform(X_test['Fuel'])
+
+X_train['Transmission'] = transmission_encoder.fit_transform(X_train['Transmission'])
+X_test['Transmission'] = transmission_encoder.transform(X_test['Transmission'])
+
+X_train['Condition'] = condition_encoder.fit_transform(X_train['Condition'])
+X_test['Condition'] = condition_encoder.transform(X_test['Condition'])
+
+X_train[['Year of manufacture', 'Mileage', 'Engine Size']] = scaler.fit_transform(X_train[['Year of manufacture', 'Mileage', 'Engine Size']])
+X_test[['Year of manufacture', 'Mileage', 'Engine Size']] = scaler.transform(X_test[['Year of manufacture', 'Mileage', 'Engine Size']])
+
+# Save the encoders and scaler
+joblib.dump(make_encoder, "make_encoder.joblib",compress=3)
+joblib.dump(fuel_encoder, "fuel_encoder.joblib",compress=3)
+joblib.dump(transmission_encoder, "transmission_encoder.joblib",compress=3)
+joblib.dump(condition_encoder, "condition_encoder.joblib",compress=3)
+joblib.dump(scaler, "scaler.joblib",compress=3)
+
+"""#### Needed Model"""
+
+# Initialize the models
+rf_model = RandomForestRegressor(random_state=42)
+xgb_model = XGBRegressor(random_state=42)
+lr_model = LinearRegression()
+
+# Fit the models on the training data
+rf_model.fit(X_train, y_train)
+xgb_model.fit(X_train, y_train)
+lr_model.fit(X_train, y_train)
+
+# Make predictions on the testing data
+rf_preds = rf_model.predict(X_test)
+xgb_preds = xgb_model.predict(X_test)
+lr_preds = lr_model.predict(X_test)
+
+# Evaluate the models using root mean squared error (RMSE)
+rf_rmse = mean_squared_error(y_test, rf_preds, squared=False)
+xgb_rmse = mean_squared_error(y_test, xgb_preds, squared=False)
+lr_rmse = mean_squared_error(y_test, lr_preds, squared=False)
+
+# Print the RMSE scores
+print(f"Random Forest RMSE: {rf_rmse:.2f}")
+print(f"XGBoost RMSE: {xgb_rmse:.2f}")
+print(f"Linear Regression RMSE: {lr_rmse:.2f}")
+
+#  R2 score
+rf_r2 = r2_score(y_test, rf_preds)
+print("Random Forest R2 Score:", rf_r2)
+
+
+xgb_r2 = r2_score(y_test, xgb_preds)
+print("XGBoost R2 Score:", xgb_r2)
+
+
+lr_r2 = r2_score(y_test, lr_preds)
+print("Linear Regression R2 Score:", lr_r2)
+
+joblib.dump(xgb_model, "car_model.joblib", compress=3)
+
+"""**Note: Many Models have been built, but only the needed ones were kept**"""
+
+sns.histplot(xgb_preds, label='prediction',color='red')
+sns.histplot(y_test, label='actual price', color = 'blue')
+plt.title('Prediction Vs Actual')
+plt.legend()
+plt.show()
+
+"""### Prediction"""
+
+import joblib
+def predict_car_price(make, year, condition, mileage, engine_size, fuel, transmission):
+    # Load the encoders and scaler
+    make_encoder = joblib.load("make_encoder.joblib")
+    fuel_encoder = joblib.load("fuel_encoder.joblib")
+    transmission_encoder = joblib.load("transmission_encoder.joblib")
+    condition_encoder = joblib.load("condition_encoder.joblib")
+    scaler = joblib.load("scaler.joblib")
+
+    # Preprocess the input
+    make_encoded = make_encoder.transform([make])[0]
+    numerical_value = scaler.transform([[year,mileage, engine_size]])
+    year_scaled = numerical_value[0][0]
+    mileage_scaled = numerical_value[0][1]
+    engine_size_scaled = numerical_value[0][2]
+    fuel_encoded = fuel_encoder.transform([fuel])[0]
+    condition_encoded = condition_encoder.transform([condition])[0]
+    transmission_encoded = transmission_encoder.transform([transmission])[0]
+
+    input_data = [[make_encoded, year_scaled, condition_encoded, mileage_scaled, engine_size_scaled, fuel_encoded, transmission_encoded]]
+    input_df = pd.DataFrame(input_data, columns=['Make', 'Year of manufacture', 'Condition', 'Mileage', 'Engine Size', 'Fuel', 'Transmission'])
+
+    # Make predictions
+    predicted_price = xgb_model.predict(input_df)
+    return round(predicted_price[0], 2)
+
+predict_car_price('Toyota', 2010,'Nigerian Used', 3000, 2300, 'Petrol', 'Automatic')
+
+"""### Gradio Interface"""
+
+import gradio as gr
+import joblib
+def predict_car_price(make, year, condition, mileage, engine_size, fuel, transmission):
+    # Load the encoders and scaler
+    make_encoder = joblib.load("make_encoder.joblib")
+    fuel_encoder = joblib.load("fuel_encoder.joblib")
+    transmission_encoder = joblib.load("transmission_encoder.joblib")
+    condition_encoder = joblib.load("condition_encoder.joblib")
+    scaler = joblib.load("scaler.joblib")
+
+    make_encoded = make_encoder.transform([make])[0]
+    numerical_value = scaler.transform([[year,mileage, engine_size]])
+    year_scaled = numerical_value[0][0]
+    mileage_scaled = numerical_value[0][1]
+    engine_size_scaled = numerical_value[0][2]
+    fuel_encoded = fuel_encoder.transform([fuel])[0]
+    condition_encoded = condition_encoder.transform([condition])[0]
+    transmission_encoded = transmission_encoder.transform([transmission])[0]
+    input_data = [[make_encoded, year_scaled, condition_encoded, mileage_scaled, engine_size_scaled, fuel_encoded, transmission_encoded]]
+    input_df = pd.DataFrame(input_data, columns=['Make', 'Year of manufacture', 'Condition', 'Mileage', 'Engine Size', 'Fuel', 'Transmission'])
+
+    # Make predictions
+    predicted_price = xgb_model.predict(input_df)
+    return round(predicted_price[0], 2)
+make_dropdown = gr.inputs.Dropdown(['Acura', 'Audi', 'BMW', 'Chevrolet', 'Dodge', 'Ford', 'Honda',
+       'Hyundai', 'Infiniti', 'Kia', 'Land Rover', 'Lexus', 'Mazda',
+       'Mercedes-Benz', 'Mitsubishi', 'Nissan', 'Peugeot',
+       'Pontiac', 'Toyota', 'Volkswagen', 'Volvo'], label="Make")
+condition_dropdown = gr.inputs.Dropdown(['Foreign Used', 'Nigerian Used'], label="Condition")
+fuel_dropdown = gr.inputs.Dropdown(["Petrol", "Diesel", "Electric"], label="Fuel")
+transmission_dropdown = gr.inputs.Dropdown(["Manual", "Automatic", "AMT"], label="Transmission")
+year_slider = gr.inputs.Slider(minimum=1992, maximum=2021, step=1, default=2010, label="Year")
+mileage_slider = gr.inputs.Slider(minimum=1, maximum=300000, step=10, default=80000, label="Mileage")
+engine_size_slider = gr.inputs.Slider(minimum=1, maximum=20000, step=1, default=100, label="Engine Size")
+
+iface = gr.Interface(
+fn=predict_car_price,
+inputs=[make_dropdown, year_slider, condition_dropdown, mileage_slider, engine_size_slider, fuel_dropdown, transmission_dropdown],
+outputs="number",
+title="Car Price Prediction",
+    description="Predict the price of a car based on its details, in Naira.",
+    examples=[
+        ["Toyota", 2010, "Nigerian Used", 80000, 2.0, "Petrol", "Automatic"],
+        ["Mercedes-Benz", 2015, "Foreign Used", 50000, 1000, "Diesel", "AMT"],
+    ],css=".gradio-container {background-color: lightgreen}"
+)
+
+iface.launch(share = True)

scaler.joblib

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transmission_encoder.joblib

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+version https://git-lfs.github.com/spec/v1
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