Stack_Overflow_Reputation_Linear_Regression.ipynb

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  "metadata": {
    "colab": {
      "name": "Stack Overflow Reputation - Linear Regression.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "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/connected-bsamadi/colab-notebooks/blob/master/Stack_Overflow_Reputation_Linear_Regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "Pqu4EdwlAkl6",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Stack Overflow Reputation - Linear Regression"
      ]
    },
    {
      "metadata": {
        "id": "sDp5z_WiAfXO",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Skt5jMPjhKdT",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import seaborn as sns \n",
        "\n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Sb_0fD4mdtpJ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from google.colab import auth\n",
        "auth.authenticate_user()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "UWB_GIFVduLr",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from google.cloud import bigquery\n",
        "\n",
        "project_id = 'ontario-2018'\n",
        "client = bigquery.Client(project=project_id)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "xkNqVSi_A6wk",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Ingest"
      ]
    },
    {
      "metadata": {
        "id": "ykUSV-W8b54g",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Number of people\n",
        "n=10000"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "jI7YuEOBBVk7",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "query = ('''\n",
        "WITH\n",
        "  g AS (\n",
        "  SELECT\n",
        "    author.name AS name,\n",
        "    COUNT(commit) AS num_commits,\n",
        "    COUNT(DISTINCT repo_name) AS num_repos,\n",
        "    AVG(LENGTH(subject)) AS avg_subject_len,\n",
        "    AVG(LENGTH(message)) AS avg_message_len,\n",
        "    AVG(author.time_sec) AS avg_time,\n",
        "    COALESCE(STDDEV(author.time_sec),\n",
        "      0) AS std_time\n",
        "  FROM\n",
        "    `bigquery-public-data.github_repos.sample_commits`\n",
        "  GROUP BY\n",
        "    author.name),\n",
        "  s AS (\n",
        "  SELECT\n",
        "    display_name,\n",
        "    AVG(reputation) AS reputation,\n",
        "    AVG(up_votes) AS up_votes,\n",
        "    AVG(down_votes) AS down_votes,\n",
        "    AVG(views) AS views\n",
        "  FROM\n",
        "    `bigquery-public-data.stackoverflow.users`\n",
        "  GROUP BY\n",
        "    display_name )\n",
        "SELECT\n",
        "  name,\n",
        "  num_commits,\n",
        "  num_repos,\n",
        "  avg_subject_len,\n",
        "  avg_message_len,\n",
        "  avg_time,\n",
        "  std_time,\n",
        "  reputation,\n",
        "  up_votes,\n",
        "  down_votes,\n",
        "  views\n",
        "FROM\n",
        "  g\n",
        "INNER JOIN\n",
        "  s\n",
        "ON\n",
        "  g.name=s.display_name\n",
        "LIMIT \n",
        "'''+str(n))\n",
        "job_config = bigquery.job.QueryJobConfig()\n",
        "query_job = client.query(query,job_config)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "b-N-ZaEnWO3L",
        "colab_type": "code",
        "outputId": "8d4090d7-7b68-42a8-9762-4eb51e0e227e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "cell_type": "code",
      "source": [
        "df = query_job.to_dataframe()\n",
        "df.head()"
      ],
      "execution_count": 7,
      "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>name</th>\n",
              "      <th>num_commits</th>\n",
              "      <th>num_repos</th>\n",
              "      <th>avg_subject_len</th>\n",
              "      <th>avg_message_len</th>\n",
              "      <th>avg_time</th>\n",
              "      <th>std_time</th>\n",
              "      <th>reputation</th>\n",
              "      <th>up_votes</th>\n",
              "      <th>down_votes</th>\n",
              "      <th>views</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Matthew Leffler</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.376438e+09</td>\n",
              "      <td>0.0</td>\n",
              "      <td>602.500000</td>\n",
              "      <td>913.500000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>62.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Nathan Howell</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>8.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>1.451933e+09</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1348.333333</td>\n",
              "      <td>20.333333</td>\n",
              "      <td>2.333333</td>\n",
              "      <td>135.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Tushar</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>8.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>1.450034e+09</td>\n",
              "      <td>0.0</td>\n",
              "      <td>317.835913</td>\n",
              "      <td>13.597523</td>\n",
              "      <td>26.065015</td>\n",
              "      <td>57.678019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>naoyashiga</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>8.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>1.457097e+09</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>26.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Steve Moser</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>9.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>1.449166e+09</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4081.000000</td>\n",
              "      <td>1373.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>600.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              name  num_commits  num_repos  avg_subject_len  avg_message_len  \\\n",
              "0  Matthew Leffler            1          1              4.0              4.0   \n",
              "1    Nathan Howell            1          1              8.0              9.0   \n",
              "2           Tushar            1          1              8.0              8.0   \n",
              "3       naoyashiga            1          1              8.0              9.0   \n",
              "4      Steve Moser            1          1              9.0              9.0   \n",
              "\n",
              "       avg_time  std_time   reputation     up_votes  down_votes       views  \n",
              "0  1.376438e+09       0.0   602.500000   913.500000    1.000000   62.000000  \n",
              "1  1.451933e+09       0.0  1348.333333    20.333333    2.333333  135.000000  \n",
              "2  1.450034e+09       0.0   317.835913    13.597523   26.065015   57.678019  \n",
              "3  1.457097e+09       0.0     8.000000     0.000000    0.000000   26.000000  \n",
              "4  1.449166e+09       0.0  4081.000000  1373.000000    6.000000  600.000000  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "metadata": {
        "id": "qOUp4RytBWfs",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Validate"
      ]
    },
    {
      "metadata": {
        "id": "hprD3i3Oyf43",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "56b99405-faa6-4ba5-bff2-8517137a1159"
      },
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "name               0\n",
              "num_commits        0\n",
              "num_repos          0\n",
              "avg_subject_len    0\n",
              "avg_message_len    0\n",
              "avg_time           0\n",
              "std_time           0\n",
              "reputation         0\n",
              "up_votes           0\n",
              "down_votes         0\n",
              "views              0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "metadata": {
        "id": "3ceeSgZp34YG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 511
        },
        "outputId": "e3147087-e2bc-4141-f796-f3c1f5e403f6"
      },
      "cell_type": "code",
      "source": [
        "sns.set(rc={'figure.figsize':(11.7,8.27)})\n",
        "sns.distplot(df['reputation'], bins=30)\n",
        "plt.show()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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3/7L3EzxSSZIkgWHcbf73vSqaW0JGDuvf4T+siwQBV16SzWcvHUxLGLL859v4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\n/iQOTZJOWwk9fQCSdCYrLCz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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fcc65508358>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "KkGHCkNv36AK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 586
        },
        "outputId": "9e533f8e-668a-4f50-f8eb-69ded70bd674"
      },
      "cell_type": "code",
      "source": [
        "correlation_matrix = df.corr().round(2)\n",
        "# annot = True to print the values inside the square\n",
        "sns.heatmap(data=correlation_matrix, annot=True)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fcc654222b0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Vl48jNWrU0HlNw0aNdNplZ2WRlZmJR4MG7PnxR+bPm8PLL3VnSOAbXL58mZdf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\nhBBCCHkv/T/5BGw9MMnckgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fcc654ae0b8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "4UZGKVsgBX52",
        "colab_type": "code",
        "outputId": "ba0f0f84-f2c9-4799-e090-04c31999cbfa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "df.reputation.max()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "261410.5"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "metadata": {
        "id": "dIWEDvl0kW4_",
        "colab_type": "code",
        "outputId": "82b9cebe-6cd3-410f-f21a-b42a39dfac38",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "df.reputation.min()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "metadata": {
        "id": "HPPVxPKKkaQK",
        "colab_type": "code",
        "outputId": "f4f650a0-340d-432b-feb4-14d79e10d2a7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "df.reputation.mean()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "763.1608219547252"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "metadata": {
        "id": "rIDLbSmMBZHW",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Transform"
      ]
    },
    {
      "metadata": {
        "id": "E0gIdH9C00PQ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "X = pd.DataFrame(df,columns=['num_commits','num_repos','avg_subject_len', 'avg_message_len', 'avg_time', 'up_votes', 'down_votes', 'views'])\n",
        "Y = df['reputation']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "USNvPxJn0qK2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "9ebce538-345b-4e97-d87a-d248840d02b0"
      },
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2, random_state=5)\n",
        "print(X_train.shape)\n",
        "print(X_test.shape)\n",
        "print(Y_train.shape)\n",
        "print(Y_test.shape)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(4150, 8)\n",
            "(1038, 8)\n",
            "(4150,)\n",
            "(1038,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "X-JTmDs8gzGS",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Build Model"
      ]
    },
    {
      "metadata": {
        "id": "fJ-n6m8mg4eS",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import mean_squared_error\n",
        "\n",
        "lin_model = LinearRegression()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "H42JP2i_BbOB",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Train"
      ]
    },
    {
      "metadata": {
        "id": "mQrASBv1Bce3",
        "colab_type": "code",
        "outputId": "2120b685-0a45-4852-923a-9eb59ce43484",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "lin_model.fit(X_train, Y_train)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "metadata": {
        "id": "0FUyLjUCBggx",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Evaluate"
      ]
    },
    {
      "metadata": {
        "id": "Cmd5p_5sBicJ",
        "colab_type": "code",
        "outputId": "449f1057-b1bd-4463-fccf-19797646800a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "\n",
        "# model evaluation for training set\n",
        "y_train_predict = lin_model.predict(X_train)\n",
        "rmse = (np.sqrt(mean_squared_error(Y_train, y_train_predict)))\n",
        "r2 = r2_score(Y_train, y_train_predict)\n",
        "\n",
        "print(\"The model performance for training set\")\n",
        "print(\"--------------------------------------\")\n",
        "print('RMSE is {}'.format(rmse))\n",
        "print('R2 score is {}'.format(r2))\n",
        "print(\"\\n\")\n",
        "\n",
        "# model evaluation for testing set\n",
        "y_test_predict = lin_model.predict(X_test)\n",
        "rmse = (np.sqrt(mean_squared_error(Y_test, y_test_predict)))\n",
        "r2 = r2_score(Y_test, y_test_predict)\n",
        "\n",
        "print(\"The model performance for testing set\")\n",
        "print(\"--------------------------------------\")\n",
        "print('RMSE is {}'.format(rmse))\n",
        "print('R2 score is {}'.format(r2))"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The model performance for training set\n",
            "--------------------------------------\n",
            "RMSE is 2903.787784712639\n",
            "R2 score is 0.6851075094918555\n",
            "\n",
            "\n",
            "The model performance for testing set\n",
            "--------------------------------------\n",
            "RMSE is 1608.177045660366\n",
            "R2 score is 0.9702706143412759\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "wrAgjAFtBi60",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Validate"
      ]
    },
    {
      "metadata": {
        "id": "oVh95RcE3X1c",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 511
        },
        "outputId": "13584f64-dec9-43b4-ffb4-3f34826414f7"
      },
      "cell_type": "code",
      "source": [
        "plt.scatter(Y_test, y_test_predict)\n",
        "plt.xlabel('True Reputation')\n",
        "plt.ylabel('Predicted Reputation')\n",
        "plt.axis('equal')\n",
        "plt.xlim(plt.xlim())\n",
        "plt.ylim(plt.ylim())\n",
        "_ = plt.plot([-10000, 350000], [-10000, 350000])"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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TfvCDH2js2LG+GBsAhD0iGgACl8uQLikpUVJSkoqKijRgwAANGDDA8VpxcbG6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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fcc64fd2ef0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "F6VL4zk3BnnV",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Push"
      ]
    },
    {
      "metadata": {
        "id": "AZxokpA8Booz",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "BSQIiG-DBpLV",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Serve"
      ]
    },
    {
      "metadata": {
        "id": "wVElqVq7Bp_m",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}