Board_Game_Geek.ipynb

{
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  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [],
      "mount_file_id": "1Wq00XztTwP5Y-TqvnF-YHBp_00rAP6qp",
      "authorship_tag": "ABX9TyPWUvUIJeqn6pvJEYpEM94J",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mralamdari/Machine_Learning_Projects/blob/main/Board_Game_Geek.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "79hEcdMhFYPB"
      },
      "source": [
        "[Board Game Geek](https://github.com/ThaWeatherman/scrapers/tree/master/boardgamegeek)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bf0_XmsCD-tm"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from sklearn import metrics\n",
        "from sklearn import ensemble\n",
        "from sklearn import linear_model\n",
        "from sklearn import preprocessing\n",
        "from sklearn import model_selection"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pvzPzo3SFIUp"
      },
      "source": [
        "data = pd.read_csv('/content/drive/MyDrive/Colab Notebooks/ML Projects/games.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lTfXvqUVI5U-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 776
        },
        "outputId": "9ec7e6de-f3a6-4285-fad9-b59f80682929"
      },
      "source": [
        "data.keys()\n",
        "data.info()\n",
        "data.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 73594 entries, 0 to 81311\n",
            "Data columns (total 20 columns):\n",
            " #   Column                Non-Null Count  Dtype  \n",
            "---  ------                --------------  -----  \n",
            " 0   id                    73594 non-null  int64  \n",
            " 1   type                  73594 non-null  object \n",
            " 2   name                  73594 non-null  object \n",
            " 3   yearpublished         73594 non-null  float64\n",
            " 4   minplayers            73594 non-null  float64\n",
            " 5   maxplayers            73594 non-null  float64\n",
            " 6   playingtime           73594 non-null  float64\n",
            " 7   minplaytime           73594 non-null  float64\n",
            " 8   maxplaytime           73594 non-null  float64\n",
            " 9   minage                73594 non-null  float64\n",
            " 10  users_rated           73594 non-null  int64  \n",
            " 11  average_rating        73594 non-null  float64\n",
            " 12  bayes_average_rating  73594 non-null  float64\n",
            " 13  total_owners          73594 non-null  int64  \n",
            " 14  total_traders         73594 non-null  int64  \n",
            " 15  total_wanters         73594 non-null  int64  \n",
            " 16  total_wishers         73594 non-null  int64  \n",
            " 17  total_comments        73594 non-null  int64  \n",
            " 18  total_weights         73594 non-null  int64  \n",
            " 19  average_weight        73594 non-null  float64\n",
            "dtypes: float64(10), int64(8), object(2)\n",
            "memory usage: 11.8+ MB\n"
          ],
          "name": "stdout"
        },
        {
          "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>id</th>\n",
              "      <th>yearpublished</th>\n",
              "      <th>minplayers</th>\n",
              "      <th>maxplayers</th>\n",
              "      <th>playingtime</th>\n",
              "      <th>minplaytime</th>\n",
              "      <th>maxplaytime</th>\n",
              "      <th>minage</th>\n",
              "      <th>users_rated</th>\n",
              "      <th>average_rating</th>\n",
              "      <th>bayes_average_rating</th>\n",
              "      <th>total_owners</th>\n",
              "      <th>total_traders</th>\n",
              "      <th>total_wanters</th>\n",
              "      <th>total_wishers</th>\n",
              "      <th>total_comments</th>\n",
              "      <th>total_weights</th>\n",
              "      <th>average_weight</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>73594.000000</td>\n",
              "      <td>73594.000000</td>\n",
              "      <td>73594.000000</td>\n",
              "      <td>73594.000000</td>\n",
              "      <td>73594.000000</td>\n",
              "      <td>73594.000000</td>\n",
              "      <td>73594.000000</td>\n",
              "      <td>73594.000000</td>\n",
              "      <td>73594.000000</td>\n",
              "      <td>73594.000000</td>\n",
              "      <td>73594.000000</td>\n",
              "      <td>73594.000000</td>\n",
              "      <td>73594.000000</td>\n",
              "      <td>73594.000000</td>\n",
              "      <td>73594.000000</td>\n",
              "      <td>73594.000000</td>\n",
              "      <td>73594.000000</td>\n",
              "      <td>73594.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>71153.788651</td>\n",
              "      <td>1995.014594</td>\n",
              "      <td>1.992635</td>\n",
              "      <td>5.711104</td>\n",
              "      <td>52.958257</td>\n",
              "      <td>50.503642</td>\n",
              "      <td>52.958257</td>\n",
              "      <td>7.151588</td>\n",
              "      <td>178.522502</td>\n",
              "      <td>4.395985</td>\n",
              "      <td>1.270685</td>\n",
              "      <td>289.342582</td>\n",
              "      <td>10.155828</td>\n",
              "      <td>13.942237</td>\n",
              "      <td>46.939886</td>\n",
              "      <td>54.251719</td>\n",
              "      <td>18.165815</td>\n",
              "      <td>0.968484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>59047.245714</td>\n",
              "      <td>81.409466</td>\n",
              "      <td>0.911922</td>\n",
              "      <td>58.761940</td>\n",
              "      <td>355.752631</td>\n",
              "      <td>343.760148</td>\n",
              "      <td>355.752631</td>\n",
              "      <td>5.013515</td>\n",
              "      <td>1203.328293</td>\n",
              "      <td>3.005607</td>\n",
              "      <td>2.422741</td>\n",
              "      <td>1578.993639</td>\n",
              "      <td>41.500099</td>\n",
              "      <td>63.723714</td>\n",
              "      <td>251.100773</td>\n",
              "      <td>298.919892</td>\n",
              "      <td>121.783251</td>\n",
              "      <td>1.192931</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-3500.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>20167.250000</td>\n",
              "      <td>1991.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>41993.500000</td>\n",
              "      <td>2005.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>5.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>128300.750000</td>\n",
              "      <td>2011.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>6.788543</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>66.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>184451.000000</td>\n",
              "      <td>2018.000000</td>\n",
              "      <td>99.000000</td>\n",
              "      <td>11299.000000</td>\n",
              "      <td>60120.000000</td>\n",
              "      <td>60120.000000</td>\n",
              "      <td>60120.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>53680.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>8.221860</td>\n",
              "      <td>73188.000000</td>\n",
              "      <td>1395.000000</td>\n",
              "      <td>1586.000000</td>\n",
              "      <td>6402.000000</td>\n",
              "      <td>11798.000000</td>\n",
              "      <td>5996.000000</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  id  yearpublished  ...  total_weights  average_weight\n",
              "count   73594.000000   73594.000000  ...   73594.000000    73594.000000\n",
              "mean    71153.788651    1995.014594  ...      18.165815        0.968484\n",
              "std     59047.245714      81.409466  ...     121.783251        1.192931\n",
              "min         1.000000   -3500.000000  ...       0.000000        0.000000\n",
              "25%     20167.250000    1991.000000  ...       0.000000        0.000000\n",
              "50%     41993.500000    2005.000000  ...       0.000000        0.000000\n",
              "75%    128300.750000    2011.000000  ...       3.000000        2.000000\n",
              "max    184451.000000    2018.000000  ...    5996.000000        5.000000\n",
              "\n",
              "[8 rows x 18 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WoOkwJQHJmOe"
      },
      "source": [
        "data.average_rating.hist()\n",
        "# as we see there are some games that has never releast and so they have zero ratings and have to be deleted\n",
        "data.drop(data[data.users_rated == 0].index, axis=0, inplace=True)\n",
        "data.dropna(axis=0, inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "zrpk6bWDOqC8",
        "outputId": "1292cb2a-ee77-41bd-d311-06290953743f"
      },
      "source": [
        "data.average_rating.hist()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f69d1de2150>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        },
        {
          "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": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 387
        },
        "id": "GURqbW6iQS2g",
        "outputId": "437aa468-d8f1-4526-82d6-5b60da2a3b88"
      },
      "source": [
        "sns.heatmap(data.corr())\n",
        "# darker ===> less corrolation\n",
        "# brighter ===> More corrolation"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f69c73b8f10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NwTmz0O-SHux"
      },
      "source": [
        "x = data.drop(columns=['bayes_average_rating', 'average_weight', 'id', 'type','name'])\n",
        "y = data.average_weight\n",
        "train_x, test_x, train_y, test_y = model_selection.train_test_split(x, y, random_state=45, test_size=0.2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FporrVFSUQCv"
      },
      "source": [
        "lr = linear_model.LinearRegression()\n",
        "lr.fit(train_x, train_y)\n",
        "h1 = lr.predict(test_x)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_2Uu31VxUmY8",
        "outputId": "110cb9f6-0b1c-4679-eef6-30f4842a4b70"
      },
      "source": [
        "metrics.mean_squared_error(h1, test_y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.1085506322567367"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8Y2L-UYuVExr"
      },
      "source": [
        "rfr=ensemble.RandomForestRegressor(n_estimators=200, min_samples_leaf=20, random_state=23)\n",
        "rfr.fit(train_x, train_y)\n",
        "h = rfr.predict(test_x)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7H6OoUN_VWfi",
        "outputId": "e3715711-6b40-4802-d5d4-3c35f1a902f7"
      },
      "source": [
        "metrics.mean_squared_error(h, test_y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.2326763161970122"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6S3uAWNkWS-O",
        "outputId": "ab3f3ada-c7ed-493c-da20-13c5c5d5cd82"
      },
      "source": [
        "n=3766\n",
        "test_1 = test_x.iloc[n].values.reshape(1, -1)\n",
        "print(test_y.iloc[n])\n",
        "print(test_x.iloc[n])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.7671\n",
            "yearpublished     2005.00000\n",
            "minplayers           2.00000\n",
            "maxplayers           2.00000\n",
            "playingtime         30.00000\n",
            "minplaytime         30.00000\n",
            "maxplaytime         30.00000\n",
            "minage              10.00000\n",
            "users_rated       1197.00000\n",
            "average_rating       6.86001\n",
            "total_owners      2029.00000\n",
            "total_traders       45.00000\n",
            "total_wanters      143.00000\n",
            "total_wishers      347.00000\n",
            "total_comments     480.00000\n",
            "total_weights      146.00000\n",
            "Name: 958, dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E53LhwLAXLMy",
        "outputId": "f4786376-b0b0-4757-ea8a-117410f8bf0a"
      },
      "source": [
        "h1=lr.predict(test_1)\n",
        "h2=rfr.predict(test_1)\n",
        "h1, h2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([2.34916397]), array([2.12756727]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 119
        }
      ]
    }
  ]
}