Second_Hand_Car_Price_Determination.ipynb

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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "outputId": "9bc296dc-cdad-434f-dd8f-72191ebbfcff"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-ebfc1ac8-29b2-47ce-93ee-bdaa77f318d5\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-ebfc1ac8-29b2-47ce-93ee-bdaa77f318d5\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving car data.csv to car data (2).csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5Gq0q73Fp8zh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "a7e9847d-2929-47f2-9567-146e9a38c2ca"
      },
      "source": [
        "import io\n",
        "df = pd.read_csv(io.BytesIO(uploaded['car data.csv']))\n",
        "df.replace(to_replace =[\"Petrol\",\"Diesel\",\"CNG\",\"Manual\",\"Automatic\"]\n",
        "                 ,value =[1,2,3,1,2])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Car_Name</th>\n",
              "      <th>Year</th>\n",
              "      <th>Selling_Price</th>\n",
              "      <th>Present_Price</th>\n",
              "      <th>Kms_Driven</th>\n",
              "      <th>Fuel_Type</th>\n",
              "      <th>Seller_Type</th>\n",
              "      <th>Transmission</th>\n",
              "      <th>Owner</th>\n",
              "      <th>Age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ritz</td>\n",
              "      <td>2014</td>\n",
              "      <td>3.35</td>\n",
              "      <td>5.59</td>\n",
              "      <td>27000</td>\n",
              "      <td>1</td>\n",
              "      <td>Dealer</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>sx4</td>\n",
              "      <td>2013</td>\n",
              "      <td>4.75</td>\n",
              "      <td>9.54</td>\n",
              "      <td>43000</td>\n",
              "      <td>2</td>\n",
              "      <td>Dealer</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ciaz</td>\n",
              "      <td>2017</td>\n",
              "      <td>7.25</td>\n",
              "      <td>9.85</td>\n",
              "      <td>6900</td>\n",
              "      <td>1</td>\n",
              "      <td>Dealer</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>wagon r</td>\n",
              "      <td>2011</td>\n",
              "      <td>2.85</td>\n",
              "      <td>4.15</td>\n",
              "      <td>5200</td>\n",
              "      <td>1</td>\n",
              "      <td>Dealer</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>swift</td>\n",
              "      <td>2014</td>\n",
              "      <td>4.60</td>\n",
              "      <td>6.87</td>\n",
              "      <td>42450</td>\n",
              "      <td>2</td>\n",
              "      <td>Dealer</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>6</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>296</th>\n",
              "      <td>city</td>\n",
              "      <td>2016</td>\n",
              "      <td>9.50</td>\n",
              "      <td>11.60</td>\n",
              "      <td>33988</td>\n",
              "      <td>2</td>\n",
              "      <td>Dealer</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>297</th>\n",
              "      <td>brio</td>\n",
              "      <td>2015</td>\n",
              "      <td>4.00</td>\n",
              "      <td>5.90</td>\n",
              "      <td>60000</td>\n",
              "      <td>1</td>\n",
              "      <td>Dealer</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>298</th>\n",
              "      <td>city</td>\n",
              "      <td>2009</td>\n",
              "      <td>3.35</td>\n",
              "      <td>11.00</td>\n",
              "      <td>87934</td>\n",
              "      <td>1</td>\n",
              "      <td>Dealer</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>299</th>\n",
              "      <td>city</td>\n",
              "      <td>2017</td>\n",
              "      <td>11.50</td>\n",
              "      <td>12.50</td>\n",
              "      <td>9000</td>\n",
              "      <td>2</td>\n",
              "      <td>Dealer</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>300</th>\n",
              "      <td>brio</td>\n",
              "      <td>2016</td>\n",
              "      <td>5.30</td>\n",
              "      <td>5.90</td>\n",
              "      <td>5464</td>\n",
              "      <td>1</td>\n",
              "      <td>Dealer</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>301 rows × 10 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "    Car_Name  Year  Selling_Price  ...  Transmission  Owner  Age\n",
              "0       ritz  2014           3.35  ...             1      0    6\n",
              "1        sx4  2013           4.75  ...             1      0    7\n",
              "2       ciaz  2017           7.25  ...             1      0    3\n",
              "3    wagon r  2011           2.85  ...             1      0    9\n",
              "4      swift  2014           4.60  ...             1      0    6\n",
              "..       ...   ...            ...  ...           ...    ...  ...\n",
              "296     city  2016           9.50  ...             1      0    4\n",
              "297     brio  2015           4.00  ...             1      0    5\n",
              "298     city  2009           3.35  ...             1      0   11\n",
              "299     city  2017          11.50  ...             1      0    3\n",
              "300     brio  2016           5.30  ...             1      0    4\n",
              "\n",
              "[301 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qH6BKwMCqGVR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "163bba2d-d8b3-4439-9f3c-c06f4b593947"
      },
      "source": [
        "df.plot(x='Kms_Driven',y='Selling_Price', style='o')\n",
        "plt.title('Kms Driven vs Selling price')\n",
        "plt.xlabel('Kms Driven')\n",
        "plt.ylabel('Selling price')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DpMq4WyFqL7e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "afafb72d-d819-48cd-d7bd-a38abc358584"
      },
      "source": [
        "df.plot(x='Present_Price',y='Selling_Price', style='o')\n",
        "plt.title('Present Price vs Selling price')\n",
        "plt.xlabel('Present Price')\n",
        "plt.ylabel('Selling price')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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qxYFS6xwmZlkwZAN/c3Ozp/vLoFw/vkf1mPXdkA38ll21uCfDLEuGbB+/mZn1jQO/mVnGOPCbmWWMA7+ZWcY48JuZZYwDv5lZxjjwm5lljAO/mVnG1CzwSxou6ReSfiVpjaTL0vLxkpZKelzSzZJeUqs6mJnZrmrZ4n8BmBERRwJTgDdJOg74D+DzEXEw8AzwvhrWwczMeqhZ4I/ElvRpc/oTwAzg1rT8BmB2repgZma7qmkfv6QmSauAp4C7gN8CnRGxPV1lPeCkK2ZmA6imgT8iuiNiCjAGeBVwWLnbSjpX0jJJy5x62cysegZkVE9EdAL3AscDrZJyWUHHAB1FtrkmItojon3kyJEDUU0zs0yo5aiekZJa08ctwEzgEZIPgHekq50JfK9WdTAzs13VMh//KOAGSU0kHzDfiYhFktYC35Y0H1gJXFvDOpiZWQ81C/wR8RAwtUD570j6+83MrA58566ZWcY48JuZZYwDv5lZxjjwm5lljAO/mVnGOPCbmWWMA7+ZWcY48JuZZYwDv5lZxjjwm5lljAO/mVnGOPCbmWWMA7+ZWcY48JuZZYwDv5lZxjjwm5lljAO/mVnGOPCbmWWMA7+ZWcY48JuZZUzNAr+kAyTdK2mtpDWSPpKWXyqpQ9Kq9OekWtXBzMx2tVsN970d+HhErJC0F7Bc0l3pss9HxGdreGwzMyuiZoE/IjYAG9LHz0p6BGir1fHMzKw8A9LHL2kcMBVYmhZ9WNJDkq6TtE+Rbc6VtEzSso0bNw5ENc3MMqHmgV/SnsBtwIUR8VfgK8BBwBSSbwSfK7RdRFwTEe0R0T5y5MhaV9PMLDNqGvglNZME/W9FxO0AEfHniOiOiBeBrwGvqmUdzMxsZ7Uc1SPgWuCRiLgqr3xU3mpvAx6uVR3MzGxXtRzVMw04A1gtaVVa9kngdElTgADWAR+oYR3MzKyHWo7q+TmgAot+UKtjWuNbuLKDKxc/yh87uxjd2sLcWROYPdWDxcwqUcsWv1lVLVzZwUW3r6ZrWzcAHZ1dXHT7agAHf7MKOGWDDRlXLn50R9DP6drWzZWLH61TjcyGJrf4rVfV6F65eOFqblr6JN0RNEmcfuwBzJ89uaJ9/LGzq6JyMyvMLX4rKde90tHZRfD37pWFKzvK3sfFC1fzzQefoDsCgO4IvvngE1y8cHVFdRnd2lJRuZkV5sBvJVWje+WmpU9WVF7M3FkTaGlu2qmspbmJubMmVLQfs6xzV4+VVI3ulVxLv9zyYnLdSx7VY9Y/DvxW0ujWFjoKBPlKuleapIJBvkmFRvuWNntqmwO9WT+V1dUjqUWSv09nUDW6V04/9oCKys2stnoN/JJOAVYBP0qfT5F0R60rZoPD7KltXHHaZNpaWxDQ1trCFadNrqjVPX/2ZN573NgdLfwmifceN7biUT1mVh2KXvpZJS0HZgBLImJqWrY6Igbsv7a9vT2WLVs2UIczM2sIkpZHRHvP8nK6erZFxOYeZZVdlTMzs0GjnIu7ayS9G2iSdAhwAfC/ta2WmZnVSjkt/vOBScALwI3AZuDCWlbKzMxqp9cWf0T8DfhU+mNmZkNcOaN67pLUmvd8H0mLa1stMzOrlXL6+PeLiM7ck4h4RtLLa1gnG2ScA9+ssZQT+F+UNDYingCQdCAe1ZMZzoFv1njKubj7KeDnkv5H0jeBnwEX1bZaNlg4B75Z4ynn4u6PJB0FHJcWXRgRm2pbLRssnAPfrPEUbfFLOiz9fRQwFvhj+jM2LStJ0gGS7pW0VtIaSR9Jy/dNLxg/lv7epzovxWphsOXAX7iyg2kL7mH8vDuZtuCeiuYFMLNEqRb/x4Bzgc8VWBYkaRxK2Q58PCJWSNoLWC7pLuAs4CcRsUDSPGAe8ImKa24DYu6sCTv18UP9cuD7eoNZdRQN/BFxrqRhwMURcX+lO46IDcCG9PGzkh4B2oC3AtPT1W4AluDAX1elRu0Mphz4pa43OPCbla9kH39EvCjpS8DU/hxE0rh0H0uB/dMPBYA/AfsX2eZckm8cjB07tj+HtxLKaUUPlhz4vt5gVh3ljOr5iaS3S32YNQOQtCdwG8lF4b/mL4skNWjBoaERcU1EtEdE+8iRI/tyaCvDUBq1M9iuN5gNVeUE/g8AtwBbJf1V0rOS/trbRgCSmkmC/rci4va0+M+SRqXLRwFP9aHeViVDqRXtOXfNqqPXwB8Re0XEsIhojoiXps9f2tt26TeEa4FHIuKqvEV3AGemj88EvteXilt1DKVWdDUmhTGz8qdePE3SVZI+J2l2mfueBpwBzJC0Kv05CVgAzJT0GPDG9LnViVvRZtnT6w1ckv4LOBi4KS36oKSZEXFeqe0i4udAsesCb6iollYzg2nUTm88nNOsOsqZevHXwMT0QizpEM81ETFxAOoHeOpFS0xbcA8dBa49tLW2cP+83m4rMcue/ky9+DjJnbs5B6RlZgNqKF2INhvMygn8ewGPSFoi6V5gLfBSSXdIuqO21TP7u6F0IdpsMCsnLfO/1bwWVnWNmEN/MKWPMBvKysnO+dOBqIhVT6NeBB1KF6LNBrNyWvw2xDRyTpvBkj7CbCgraxy/DS2+CGpmpTjwNyBfBDWzUsq5gWs1uyZS2wwsA+ZHxNO1qJhVLndBt6OzC7HzH80XQc0sp5w+/h8C3cCN6fN3AXuQpFS+HjilJjWzivS8oBuwI/i3+SKomeUpJ/C/MSLyp1pcLWlFRBwl6b21qphVptAF3VzQ912tZpavnD7+Jkmvyj2RdAyQy+q1vSa1sor5gq6ZlaucFv/7gevSCVUE/BV4v6QRwBW1rJyVb3RrS8E8Nr6ga2Y9lZOP/5cRMRmYAhwZEUdExC8i4rmI+E7tq2jlcHplMytXOaN6dgfeDowDdsvNwBgR/17TmllFBvtdrY2YQsJsqCqnq+d7JMM3lwMv1LY61h+D9a7WRk0hYTZUlRP4x0TEm2peE2tYjZxCwmwoKmdUz/9KmlzzmljD8ogjs8GlnMD/GmC5pEclPSRptaSHal0xaxxOIWE2uJQT+N8MHAKcSHKX7smUcbeupOskPSXp4byySyV19Jh83RqcRxyZDS5FA7+kl6YPny3y05vrgULXBj4fEVPSnx9UVl0bimZPbePtR7fRlI4Ia5J4+9GD80K0WRaUurh7I0nrfjl/T/2SE8ArSu04In4maVw/65dZjTT8ceHKDm5b3kF3JGnjuiO4bXkH7QfuO2Rfk9lQVrTFHxEnp7/HR8Qr0t+5n5JBvxcfTq8VXCdpn37sp2Hlhj92dHYR/H3448KVHfWuWp+UGtVjZgOvVFfPUaV++ni8rwAHkdwFvAH4XInjnytpmaRlGzdu7OPhhqZGC5SFUkmUKjez2irV1VM0KJN09VSc8jEi/px7LOlrwKIS614DXAPQ3t7ecz6AhlaL4Y/17DpqknZ08/QsN7OBVzTwR8Trq30wSaMiYkP69G3Aw6XWz6pqJ1yr952zhYJ+qXIzq62igV/SaaU2jIjbSy2XdBMwHdhP0nrgEmC6pCkk3xjWAR+osL6ZMHfWhJ0CNfRv+GO975xtK/JB1uZx/GZ1Uaqrp9RY/QBKBv6IOL1A8bXlVCrrqp1wrd53zlb7g8zM+qdUV8/ZA1kR21k1E67VO1f/YM8capY15aRl3h/4DDA6It4s6ZXA8RHh1vsQMRha3IM1c6hZFpWTsuF6YDEwOn3+G+DCWlXIqm/21DauOG0yba0tiKRv/YrTJjsQm2VUOWmZ94uI70i6CCAitkvq7m0jq0yth1u6xW1mOeUE/uckvYzkgi6SjiOZmMWqpN7DLc0sW8rp6vkYcAdwkKT7gf8Gzq9prTKm0e7UNbPBrdcWf0SskHQCMIEkUdujEbGt5jXLkHoPtzSzbCmVq+cYSf8ASb8+cDRwOfA5SfsOUP0yoXWP5orKzcz6o1RXz9XAVgBJrwMWkHTzbCbNoWPVUSxzgTMamFktlOrqaYqIv6SP5wDXRMRtwG2SVtW+atmxuatwz1mxcjOz/ijV4m+SlPtgeANwT96yckYDWZmK3UE7TGL8vDuZtuCeIZuL38wGn1IB/Cbgp5I2AV3AfQCSDsbDOfstf9x+6x7NNA8T217cuW8nl73SwzvNrJpKzcB1OfBxkjt3XxOxo8d5GB7O2S89Z9h65m/bQNDa0owonKfewzvNrFpKdtlExIMFyn5Tu+pkQ6Fx+9u6g860T79YnvqOzi7Gz7vTSc7MrF/KuYHLqqw/4/MbYQ5eM6svB/46qEY6ZHf9mFlfOfDXwdxZE2hpbur3fnxnr5n1hYdl1kHPiUn6ep/WQE2kYmaNxYG/TvLTJI+bd2fR9USSumHL89t3Gu7pqQvNrK8c+AeBffZoToZ0Fihf+W8nArXP129m2VGzwC/pOuBk4KmIODwt2xe4GRgHrAP+MSKeqVUdhorntxWe1ya/3BOpmFm11PLi7vXAm3qUzQN+EhGHAD9Jn2de17YXKyo3M+uPmrX4I+Jnksb1KH4rMD19fAOwBPhErepgCXcTmVm+ge7j3z8iNqSP/wTsX2xFSecC5wKMHTt2AKpWe8UC8IiXNPHc1l27e0a8pP9DPj2to5n1VLeLuxERkoqOZIyIa0jz/re3tw+ZzPS54N7R2UWTRHcEba0tvP6wkdy2vKNgAG5uGgbsGviT8v4pNa2jA79ZNg104P+zpFERsUHSKOCpAT5+TS1c2cHcW361Y9hlfnbNbz34xC7j9XMBuJb5+DuK3ORVrNzMGt9A37l7B3Bm+vhM4HsDfPyauvSONbukVs4p9pWlI03LXEg1pl4slOmzVLmZNb6aBX5JNwEPABMkrZf0PpLpG2dKegx4Y/q8ISxc2bEju2YlRPHhnNWYerFYps9i5WbW+Go5quf0IoveUKtj1kvuAmpfBMWHbfblg6SnttaWgt06bU73YJZZTtJWBYUuoPbUl6Rsw6rQG1MoIZzTPZhlmwN/FfSWJbOleRhXnDa54lZ2kcsFFZk9tW3HsUXS0r/itMke0WOWYc7VUwWji3Sn5AxvbtoRaPPH1Jdj4cqOfgdpp3sws3xu8ffTwpUd/G3r9pLr5BKw9Wx9lzOyxpOtmFm1ucXfDz3vii3loIt+QHcETRKnH3sA82dPLmt7T7ZiZtXmwN8P5VzUzckNn+yO4JsPPsF3V3Twt63d7N3SzIsRvLC98MiecidbcT4eMyuXu3r6oT+t8ee2dhMkQzaHSRzy8hG7rFPu6JvcN4eOdDYvT8ZuZqU48PdDtaY+7NrWzd+2vsgX5kzp0+ibUvl4zMx6cldPP8ydNYGP3ryqz3Pm5vtjZ1efR98U++bh6wNmVohb/P0we2pbVYI+9O/bQ0tz4T9jsXIzyzZHhn64eGHf0jT01N87abuKXBguVm5m2ebA30cLV3bwrQef6NO2zcPEPns0V+1O2mL51pyHzcwKcR9/H132/TV97ua58p1H7hLo+zMcMzfhS6FyM7OeHPgrkD+7Vl+1tbYUDPr9mR7x9GMP4JsFvn2cfuwBfa6nmTUud/WUKX+sfH8U6svv73DM+bMnM+2gfXcqm3bQvsyfPbnvFTWzhuXAX6ZK7tItZo/mYQVb8P2dHnHhyg5WPLF5p7IVT2z2DVxmVpADfy8WruxgymU/rsoctcUmXCnVFz9+3p1MW3BPySDuG7jMrBLu4y+h5+Tp/VVsrH6paRDzUzBA4T5/38BlZpVwi7+EKxc/WrWg3zxMRcfqlzNBS6kWfLEPlHJuClu4soNpC+4p65uFmTWGugR+SeskrZa0StKyetShHNXo3skpdStVoekRCynWgu/r9IpO7maWTfVs8b8+IqZERHsd6zBgul8MLvv+moLLyp2gpVgLvq/TK/ragFk2uY9/AOVm4iokP0FboQlaajFBuq8NmGVTvQJ/AD+WFMDVEXFNzxUknQucCzB27NgBrl7tlbpTN/e73Dt5+3oDWLG5gquVbtrMBqd6Bf7XRESHpJcDd0n6dUT8LH+F9MPgGoD29vaGyDrT2tIMFA7Uc2/9FZfesYbNXdt2BPr7580oa7+lumxKBf65syYMyDcLMxtc6tLHHxEd6e+ngO8Cr6pHPQbapadOAgoH6m3dQWfXtj5dZO1rl01fr1GbQDwAAAp6SURBVA2Y2dA24IFf0ghJe+UeAycCDw90PYrJH95YK+X0oVdykbU/wznNLHvq0eLfH/i5pF8BvwDujIgf1aEeu+g5vLHacoG83IBc7kXWubMm0Dxs55FApe4byPFwTrNsGvDAHxG/i4gj059JEXH5QNehmGrk4yklF8jLHbffukdz+TdX9RwBWkZGZg/nNMsm37mbpxrDGEud0FxLv2ff+j57NBdcf8vz28tqjV+5+FG2de/8HWVbd/QawD2c0yybHPjztBYJwOXIXRy9as4UvjBnyi5dL5AE71zLffbUNu6fN4PfL3gLbzliVMF99kwXUaw13tcA7msDZtnkwJ9auLKDzhI3WPXm9wvewv3zZuwYEVMsRUOhlvtNS58s+ziFgnlfA3hfUz2Y2dDmwE8yafqFN6/q1wXd/D74y76/hu4Syd16ttxLZefsqVAw72sA93BOs2zKfMqGhSs7Ck5bWKncTVhQOjVDTn7LvdicuT0VC+aV3unbc1sHerNsyXzgr+YIlm3dxROx9ZTfci82Z+60g/Zl3dNdZQXzagTw/kz4bmZDR+YDfzVTL0PS2m9taaazq3irv2fLPTc37k1Ln6Q7giaJ0489YEDnzO3vhO9mNnRkPvCX281SiUtPncSFN68qurxQP/r82ZPrOjl6X/P9mNnQk/mLu9UO+i3phOq5hGw9tbW2DMpA6jH9ZtmR+cBfzrSHlRiejq659NRJQ2qopMf0m2VHprp63vO1B7j/t3/Z8XzaQfsyd9aEkt0y+dqK5K/Pl7sXoD8jberBKZrNsiMzgX/mVUt47KnndirL/xDozRfmTGH21DbG9ZK1M7+FXGqkzWAbQTPUPqjMrO8yEfgXruzYJejnlBP899mjeUcA3GeP5pLj9MtpIQ/WETQe02+WDZno47/o9of6vG1LcxOXnDJpx/NLTplEgTQ8ALz3uLFlBU5nxTSzemr4Fv/ClR10bSuWOae0ffZo5pJTJu0UzHOPL71jzY6x+oXWK8UjaMysnho68C9c2VH2hdtCni/ygdHfLhFPcm5m9dTQXT39CfpQu+4XZ8U0s3pq6BZ/NdSi+8UjaMysnho28Pc27LJctep+8QgaM6uXunT1SHqTpEclPS5pXrX3X+lk4c3DxHuPG+vuFzPLhAEP/JKagC8DbwZeCZwu6ZXVPEal/fLbXgzu/fVGT0piZplQj66eVwGPR8TvACR9G3grsLZaB+hLv/wfO7vc/WJmmVCPrp42IH+S2fVp2U4knStpmaRlGzdurOgAfemX91BKM8uKQTucMyKuiYj2iGgfOXJkRdtW2i/vvnwzy5J6BP4O4IC852PSsqqZPbWN/fd6SdHlh7x8hPvyzSyz6tHH/0vgEEnjSQL+u4B3V/sgSz81c5eMnLsNE59955EO8maWaQMe+CNiu6QPA4uBJuC6iChvhvIK3fWx6bXYrZnZkFaXG7gi4gfAD+pxbDOzrBu0F3fNzKw2HPjNzDLGgd/MLGMc+M3MMkYRUe869ErSRuAPfdh0P2BTlaszFPk8JHweEj4PiSychwMjYpc7YIdE4O8rScsior3e9ag3n4eEz0PC5yGR5fPgrh4zs4xx4Dczy5hGD/zX1LsCg4TPQ8LnIeHzkMjseWjoPn4zM9tVo7f4zcysBwd+M7OMadjAX+sJ3QcrSQdIulfSWklrJH0kLd9X0l2SHkt/71PvutaapCZJKyUtSp+Pl7Q0fU/cLKn4pA0NQlKrpFsl/VrSI5KOz+h74aPp/8PDkm6SNDyL74echgz8AzGh+yC2Hfh4RLwSOA44L33t84CfRMQhwE/S543uI8Ajec//A/h8RBwMPAO8ry61GlhfBH4UEYcBR5Kcj0y9FyS1ARcA7RFxOEk6+HeRzfcD0KCBn7wJ3SNiK5Cb0L3hRcSGiFiRPn6W5B+9jeT135CudgMwuz41HBiSxgBvAb6ePhcwA7g1XSUL52Bv4HXAtQARsTUiOsnYeyG1G9AiaTdgD2ADGXs/5GvUwF/WhO6NTtI4YCqwFNg/Ijaki/4E7F+nag2ULwD/B3gxff4yoDMitqfPs/CeGA9sBL6Rdnl9XdIIMvZeiIgO4LPAEyQBfzOwnOy9H3Zo1MCfeZL2BG4DLoyIv+Yvi2QMb8OO45V0MvBURCyvd13qbDfgKOArETEVeI4e3TqN/l4ASK9hvJXkg3A0MAJ4U10rVWeNGvhrPqH7YCapmSTofysibk+L/yxpVLp8FPBUveo3AKYBp0paR9LNN4Okr7s1/aoP2XhPrAfWR8TS9PmtJB8EWXovALwR+H1EbIyIbcDtJO+RrL0fdmjUwL9jQvf0Sv27gDvqXKcBkfZlXws8EhFX5S26AzgzfXwm8L2BrttAiYiLImJMRIwj+dvfExHvAe4F3pGu1tDnACAi/gQ8KWlCWvQGYC0Zei+kngCOk7RH+v+ROw+Zej/ka9g7dyWdRNLPm5vQ/fI6V2lASHoNcB+wmr/3b3+SpJ//O8BYkhTX/xgRf6lLJQeQpOnAv0bEyZJeQfINYF9gJfDeiHihnvWrNUlTSC5wvwT4HXA2SYMvU+8FSZcBc0hGva0E3k/Sp5+p90NOwwZ+MzMrrFG7eszMrAgHfjOzjHHgNzPLGAd+M7OMceA3M8sYB34b8iR1S1qVZl68RdIedajDdEmvLrLsLEkb0zqulXROkfVOzVImWasfB35rBF0RMSXNvLgV+GD+wry7M2tpOlAw8Kdujogp6XqfkbRTfhxJu0XEHRGxoHZVNEs48FujuQ84OG2B3yfpDmBtmpv/Skm/lPSQpA9AkrJA0s/yvjG8Ni0/UdIDklak3yL2TMvXSbosLV8t6bA0Gd4HgY+m+3ltscpFxFPAb4EDJV0v6auSlgL/L/1m8KX0OPtL+q6kX6U/r07L3yvpF+lxrk5TkJtVxIHfGkbasn8zyV3LkOSl+UhEHEqSa31zRBwDHAOcI2k88G5gcdoaPxJYJWk/4GLgjRFxFLAM+FjeoTal5V8huSt4HfBVktzuUyLivhJ1fAXwCuDxtGgM8OqI+FiPVf8T+GlEHJm+jjWSJpLcfTotrW838J7KzpJZkr3PbKhrkbQqfXwfSa6iVwO/iIjfp+UnAkdIyuVm2Rs4hCSv03VpYruFEbFK0gkkE/jcn6R24SXAA3nHyyW+Ww6cVmYd56TpNF4APhARf0n3fUtEdBdYfwbwTwDp8s2SzgCOBn6ZbttC4ydYsxpw4LdG0JW2gHdIA+Nz+UXA+RGxuOfGkl5HMmnL9ZKuIpmN6a6IOL3I8XL5XLop/3/o5oj4cIHy5wqUFSPghoi4qIJtzHbhrh7LisXAv6QteyQdKmmEpAOBP0fE10iSmR0FPAhMk3Rwuu4ISYf2sv9ngb2qWN+fAP+SHr8pnU3rJ8A7JL08Ld83rb9ZRRz4LSu+TpKKd4Wkh4GrSVrr04FfSVpJ0n/+xYjYCJwF3CTpIZJunsN62f/3gbf1dnG3Ah8BXi9pNUmX0isjYi3JtYcfp/W6CxhVhWNZxjg7p5lZxrjFb2aWMQ78ZmYZ48BvZpYxDvxmZhnjwG9mljEO/GZmGePAb2aWMf8f79wchUcW+csAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hPfQj70nqRFS"
      },
      "source": [
        "from sklearn import linear_model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "Vq0DimvTvFUR",
        "outputId": "8787cb73-acdd-40eb-88b6-6717801824df"
      },
      "source": [
        "dk=df.replace(to_replace =[\"Petrol\",\"Diesel\",\"CNG\",\"Manual\",\"Automatic\"]\n",
        "                 ,value =[1,2,3,1,2])\n",
        "dk.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Car_Name</th>\n",
              "      <th>Year</th>\n",
              "      <th>Selling_Price</th>\n",
              "      <th>Present_Price</th>\n",
              "      <th>Kms_Driven</th>\n",
              "      <th>Fuel_Type</th>\n",
              "      <th>Seller_Type</th>\n",
              "      <th>Transmission</th>\n",
              "      <th>Owner</th>\n",
              "      <th>Age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ritz</td>\n",
              "      <td>2014</td>\n",
              "      <td>3.35</td>\n",
              "      <td>5.59</td>\n",
              "      <td>27000</td>\n",
              "      <td>1</td>\n",
              "      <td>Dealer</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>sx4</td>\n",
              "      <td>2013</td>\n",
              "      <td>4.75</td>\n",
              "      <td>9.54</td>\n",
              "      <td>43000</td>\n",
              "      <td>2</td>\n",
              "      <td>Dealer</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ciaz</td>\n",
              "      <td>2017</td>\n",
              "      <td>7.25</td>\n",
              "      <td>9.85</td>\n",
              "      <td>6900</td>\n",
              "      <td>1</td>\n",
              "      <td>Dealer</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>wagon r</td>\n",
              "      <td>2011</td>\n",
              "      <td>2.85</td>\n",
              "      <td>4.15</td>\n",
              "      <td>5200</td>\n",
              "      <td>1</td>\n",
              "      <td>Dealer</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>swift</td>\n",
              "      <td>2014</td>\n",
              "      <td>4.60</td>\n",
              "      <td>6.87</td>\n",
              "      <td>42450</td>\n",
              "      <td>2</td>\n",
              "      <td>Dealer</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Car_Name  Year  Selling_Price  ...  Transmission  Owner  Age\n",
              "0     ritz  2014           3.35  ...             1      0    6\n",
              "1      sx4  2013           4.75  ...             1      0    7\n",
              "2     ciaz  2017           7.25  ...             1      0    3\n",
              "3  wagon r  2011           2.85  ...             1      0    9\n",
              "4    swift  2014           4.60  ...             1      0    6\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PX1Hva2Lqab-"
      },
      "source": [
        "X=dk[['Kms_Driven','Present_Price','Age','Fuel_Type','Transmission']]\n",
        "y=dk['Selling_Price']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iMrlilajqeYa"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size= 0.2, random_state=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RtY4O6bDqlnp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "29fd6da7-14ca-47c7-ed38-924cea43c1cf"
      },
      "source": [
        "regressor= linear_model.LinearRegression()\n",
        "regressor.fit(X_train,y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RbPSDi-dqmkU"
      },
      "source": [
        "y_predict=regressor.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "It7MInVuqqbS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "outputId": "d0850ce4-74c1-43d0-8b0c-b8da019f6c08"
      },
      "source": [
        "df2=pd.DataFrame({'Actual':y_test,'Predicted':y_predict})\n",
        "df2.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Actual</th>\n",
              "      <th>Predicted</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>223</th>\n",
              "      <td>8.25</td>\n",
              "      <td>7.047629</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>150</th>\n",
              "      <td>0.50</td>\n",
              "      <td>-0.030005</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>226</th>\n",
              "      <td>5.25</td>\n",
              "      <td>3.833648</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>296</th>\n",
              "      <td>9.50</td>\n",
              "      <td>8.631717</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>18.00</td>\n",
              "      <td>14.020294</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>261</th>\n",
              "      <td>5.50</td>\n",
              "      <td>4.478575</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>246</th>\n",
              "      <td>3.75</td>\n",
              "      <td>3.019003</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>166</th>\n",
              "      <td>0.45</td>\n",
              "      <td>1.982294</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>221</th>\n",
              "      <td>4.50</td>\n",
              "      <td>4.528977</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>215</th>\n",
              "      <td>4.50</td>\n",
              "      <td>4.226718</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Actual  Predicted\n",
              "223    8.25   7.047629\n",
              "150    0.50  -0.030005\n",
              "226    5.25   3.833648\n",
              "296    9.50   8.631717\n",
              "52    18.00  14.020294\n",
              "261    5.50   4.478575\n",
              "246    3.75   3.019003\n",
              "166    0.45   1.982294\n",
              "221    4.50   4.528977\n",
              "215    4.50   4.226718"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tujF5yX6qt07",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c8f129c0-f52f-4a4d-9322-d7a28cf6e984"
      },
      "source": [
        "Predicted_Price = regressor.predict([[40000,8,7,1,1]])\n",
        "print(\"Price in Lakhs\")\n",
        "print(Predicted_Price)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Price in Lakhs\n",
            "[3.9792959]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mVh0tymbqxyN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "37823458-055d-46d5-b692-7aeeb512a569"
      },
      "source": [
        "from sklearn import metrics\n",
        "print('Accuracy of the model:',metrics.r2_score(y_test,y_predict))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy of the model: 0.9113066381462803\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wV_WauSZq12Q"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    }
  ]
}