starter_ml.ipynb

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  "metadata": {
    "colab": {
      "name": "starter-ml.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/maximgeller/projects/blob/master/starter_ml.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xfqqib420567",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zgiYNyJR1euj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "outputId": "833d065d-2d9d-48be-8987-d8a56ea37279"
      },
      "source": [
        "%ls"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[0m\u001b[01;32manscombe.json\u001b[0m*                colab_2.png            \u001b[01;32mREADME.md\u001b[0m*\n",
            "california_housing_test.csv   mnist_test.csv\n",
            "california_housing_train.csv  mnist_train_small.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pLpPvNkd1kLR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "736126d5-5c1d-4870-ef74-fb66f6148b04"
      },
      "source": [
        "%cd sample_data/"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[Errno 2] No such file or directory: 'sample_data/'\n",
            "/content/sample_data\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5ZJOHOJt1oo2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "9cc9b631-32f6-43e7-eeeb-e7cd429b4127"
      },
      "source": [
        "data = pd.read_csv(\"california_housing_train.csv\")\n",
        "data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>longitude</th>\n",
              "      <th>latitude</th>\n",
              "      <th>housing_median_age</th>\n",
              "      <th>total_rooms</th>\n",
              "      <th>total_bedrooms</th>\n",
              "      <th>population</th>\n",
              "      <th>households</th>\n",
              "      <th>median_income</th>\n",
              "      <th>median_house_value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-114.31</td>\n",
              "      <td>34.19</td>\n",
              "      <td>15.0</td>\n",
              "      <td>5612.0</td>\n",
              "      <td>1283.0</td>\n",
              "      <td>1015.0</td>\n",
              "      <td>472.0</td>\n",
              "      <td>1.4936</td>\n",
              "      <td>66900.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-114.47</td>\n",
              "      <td>34.40</td>\n",
              "      <td>19.0</td>\n",
              "      <td>7650.0</td>\n",
              "      <td>1901.0</td>\n",
              "      <td>1129.0</td>\n",
              "      <td>463.0</td>\n",
              "      <td>1.8200</td>\n",
              "      <td>80100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-114.56</td>\n",
              "      <td>33.69</td>\n",
              "      <td>17.0</td>\n",
              "      <td>720.0</td>\n",
              "      <td>174.0</td>\n",
              "      <td>333.0</td>\n",
              "      <td>117.0</td>\n",
              "      <td>1.6509</td>\n",
              "      <td>85700.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-114.57</td>\n",
              "      <td>33.64</td>\n",
              "      <td>14.0</td>\n",
              "      <td>1501.0</td>\n",
              "      <td>337.0</td>\n",
              "      <td>515.0</td>\n",
              "      <td>226.0</td>\n",
              "      <td>3.1917</td>\n",
              "      <td>73400.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-114.57</td>\n",
              "      <td>33.57</td>\n",
              "      <td>20.0</td>\n",
              "      <td>1454.0</td>\n",
              "      <td>326.0</td>\n",
              "      <td>624.0</td>\n",
              "      <td>262.0</td>\n",
              "      <td>1.9250</td>\n",
              "      <td>65500.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   longitude  latitude  ...  median_income  median_house_value\n",
              "0    -114.31     34.19  ...         1.4936             66900.0\n",
              "1    -114.47     34.40  ...         1.8200             80100.0\n",
              "2    -114.56     33.69  ...         1.6509             85700.0\n",
              "3    -114.57     33.64  ...         3.1917             73400.0\n",
              "4    -114.57     33.57  ...         1.9250             65500.0\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "izvHemLG109-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "outputId": "224138e5-f887-4dd2-eaf0-5080f5ad3183"
      },
      "source": [
        "data.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "\n",
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              "        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>longitude</th>\n",
              "      <th>latitude</th>\n",
              "      <th>housing_median_age</th>\n",
              "      <th>total_rooms</th>\n",
              "      <th>total_bedrooms</th>\n",
              "      <th>population</th>\n",
              "      <th>households</th>\n",
              "      <th>median_income</th>\n",
              "      <th>median_house_value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>17000.000000</td>\n",
              "      <td>17000.000000</td>\n",
              "      <td>17000.000000</td>\n",
              "      <td>17000.000000</td>\n",
              "      <td>17000.000000</td>\n",
              "      <td>17000.000000</td>\n",
              "      <td>17000.000000</td>\n",
              "      <td>17000.000000</td>\n",
              "      <td>17000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>-119.562108</td>\n",
              "      <td>35.625225</td>\n",
              "      <td>28.589353</td>\n",
              "      <td>2643.664412</td>\n",
              "      <td>539.410824</td>\n",
              "      <td>1429.573941</td>\n",
              "      <td>501.221941</td>\n",
              "      <td>3.883578</td>\n",
              "      <td>207300.912353</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.005166</td>\n",
              "      <td>2.137340</td>\n",
              "      <td>12.586937</td>\n",
              "      <td>2179.947071</td>\n",
              "      <td>421.499452</td>\n",
              "      <td>1147.852959</td>\n",
              "      <td>384.520841</td>\n",
              "      <td>1.908157</td>\n",
              "      <td>115983.764387</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-124.350000</td>\n",
              "      <td>32.540000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.499900</td>\n",
              "      <td>14999.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-121.790000</td>\n",
              "      <td>33.930000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>1462.000000</td>\n",
              "      <td>297.000000</td>\n",
              "      <td>790.000000</td>\n",
              "      <td>282.000000</td>\n",
              "      <td>2.566375</td>\n",
              "      <td>119400.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>-118.490000</td>\n",
              "      <td>34.250000</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>2127.000000</td>\n",
              "      <td>434.000000</td>\n",
              "      <td>1167.000000</td>\n",
              "      <td>409.000000</td>\n",
              "      <td>3.544600</td>\n",
              "      <td>180400.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>-118.000000</td>\n",
              "      <td>37.720000</td>\n",
              "      <td>37.000000</td>\n",
              "      <td>3151.250000</td>\n",
              "      <td>648.250000</td>\n",
              "      <td>1721.000000</td>\n",
              "      <td>605.250000</td>\n",
              "      <td>4.767000</td>\n",
              "      <td>265000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>-114.310000</td>\n",
              "      <td>41.950000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>37937.000000</td>\n",
              "      <td>6445.000000</td>\n",
              "      <td>35682.000000</td>\n",
              "      <td>6082.000000</td>\n",
              "      <td>15.000100</td>\n",
              "      <td>500001.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          longitude      latitude  ...  median_income  median_house_value\n",
              "count  17000.000000  17000.000000  ...   17000.000000        17000.000000\n",
              "mean    -119.562108     35.625225  ...       3.883578       207300.912353\n",
              "std        2.005166      2.137340  ...       1.908157       115983.764387\n",
              "min     -124.350000     32.540000  ...       0.499900        14999.000000\n",
              "25%     -121.790000     33.930000  ...       2.566375       119400.000000\n",
              "50%     -118.490000     34.250000  ...       3.544600       180400.000000\n",
              "75%     -118.000000     37.720000  ...       4.767000       265000.000000\n",
              "max     -114.310000     41.950000  ...      15.000100       500001.000000\n",
              "\n",
              "[8 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jVtoZUqn2I27",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 758
        },
        "outputId": "a97bdb07-6059-42bb-b525-28fceabc94f1"
      },
      "source": [
        "plt.hist(data[\"median_house_value\"], bins=100)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([  5.,   3.,   5.,   7.,  11.,  40.,  76., 138., 234., 212., 276.,\n",
              "        283., 219., 244., 337., 321., 388., 316., 269., 305., 378., 307.,\n",
              "        290., 298., 281., 394., 342., 328., 354., 390., 455., 344., 345.,\n",
              "        282., 339., 345., 336., 248., 216., 217., 236., 264., 255., 292.,\n",
              "        258., 226., 212., 194., 181., 177., 166., 204., 178., 184., 141.,\n",
              "        126., 130., 127.,  95.,  71.,  80., 104.,  88., 109.,  87., 112.,\n",
              "         96., 120.,  91., 149.,  77.,  82.,  79.,  55.,  68.,  58.,  58.,\n",
              "         40.,  57.,  55.,  40.,  48.,  38.,  42.,  36.,  33.,  38.,  29.,\n",
              "         39.,  52.,  23.,  27.,  19.,  23.,  24.,  20.,  27.,  15.,  15.,\n",
              "        852.]),\n",
              " array([ 14999.  ,  19849.02,  24699.04,  29549.06,  34399.08,  39249.1 ,\n",
              "         44099.12,  48949.14,  53799.16,  58649.18,  63499.2 ,  68349.22,\n",
              "         73199.24,  78049.26,  82899.28,  87749.3 ,  92599.32,  97449.34,\n",
              "        102299.36, 107149.38, 111999.4 , 116849.42, 121699.44, 126549.46,\n",
              "        131399.48, 136249.5 , 141099.52, 145949.54, 150799.56, 155649.58,\n",
              "        160499.6 , 165349.62, 170199.64, 175049.66, 179899.68, 184749.7 ,\n",
              "        189599.72, 194449.74, 199299.76, 204149.78, 208999.8 , 213849.82,\n",
              "        218699.84, 223549.86, 228399.88, 233249.9 , 238099.92, 242949.94,\n",
              "        247799.96, 252649.98, 257500.  , 262350.02, 267200.04, 272050.06,\n",
              "        276900.08, 281750.1 , 286600.12, 291450.14, 296300.16, 301150.18,\n",
              "        306000.2 , 310850.22, 315700.24, 320550.26, 325400.28, 330250.3 ,\n",
              "        335100.32, 339950.34, 344800.36, 349650.38, 354500.4 , 359350.42,\n",
              "        364200.44, 369050.46, 373900.48, 378750.5 , 383600.52, 388450.54,\n",
              "        393300.56, 398150.58, 403000.6 , 407850.62, 412700.64, 417550.66,\n",
              "        422400.68, 427250.7 , 432100.72, 436950.74, 441800.76, 446650.78,\n",
              "        451500.8 , 456350.82, 461200.84, 466050.86, 470900.88, 475750.9 ,\n",
              "        480600.92, 485450.94, 490300.96, 495150.98, 500001.  ]),\n",
              " <a list of 100 Patch objects>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        },
        {
          "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": "l6bG7ly447QJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        },
        "outputId": "ee023a00-9c13-4205-bf94-24f566565e3a"
      },
      "source": [
        "!pip install sklearn"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: sklearn in /usr/local/lib/python3.6/dist-packages (0.0)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.6/dist-packages (from sklearn) (0.22.2.post1)\n",
            "Requirement already satisfied: scipy>=0.17.0 in /usr/local/lib/python3.6/dist-packages (from scikit-learn->sklearn) (1.4.1)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit-learn->sklearn) (0.15.1)\n",
            "Requirement already satisfied: numpy>=1.11.0 in /usr/local/lib/python3.6/dist-packages (from scikit-learn->sklearn) (1.18.5)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zzYg11Pz49RS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "X_test = pd.read_csv(\"california_housing_test.csv\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fTTQJO2t5pDR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_test = X_test[\"median_house_value\"]\n",
        "X_test = X_test.drop([\"median_house_value\"], axis = 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ok8RtmwJ551m",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_train = data[\"median_house_value\"]\n",
        "X_train = data.drop([\"median_house_value\"], axis = 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9168Yi5F6A84",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "outputId": "3f7f0f94-c9db-47eb-9a22-67a8e5c5215c"
      },
      "source": [
        "lm = LinearRegression().fit(X_train, y_train)\n",
        "\n",
        "y_pred = lm.predict(X_test)\n",
        "print(\"R-Squared value:\",lm.score(X_test,y_test))\n",
        "\n",
        "my_formatted_list = [ '%.2f' % i for i in lm.coef_ ]\n",
        "str1 = 'x + '.join(str(e) for e in my_formatted_list)\n",
        "print(\"Formula is:\\n y = \", str1, ' + ', str(lm.intercept_) )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "R-Squared value: 0.6195057678312047\n",
            "Formula is:\n",
            " y =  -43139.64x + -42925.67x + 1150.69x + -8.38x + 117.65x + -38.49x + 45.44x + 40507.07  +  -3620600.8929739078\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}