Board_game_review_predictor.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/KenichiQaz/ML_Learning/blob/main/Board_game_review_predictor.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "f48a4767",
      "metadata": {
        "id": "f48a4767"
      },
      "outputs": [],
      "source": [
        "import sys\n",
        "import pandas\n",
        "import matplotlib\n",
        "import seaborn\n",
        "import sklearn"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "c8899a37",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c8899a37",
        "outputId": "879b838f-320e-4289-8996-43e6067fdafa"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3.7.14 (default, Sep  8 2022, 00:06:44) \n",
            "[GCC 7.5.0]\n",
            "1.3.5\n",
            "3.2.2\n",
            "0.11.2\n",
            "1.0.2\n"
          ]
        }
      ],
      "source": [
        "print(sys.version)\n",
        "print(pandas.__version__)\n",
        "print(matplotlib.__version__)\n",
        "print(seaborn.__version__)\n",
        "print(sklearn.__version__)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "abb8d318",
      "metadata": {
        "id": "abb8d318"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N7pogvfu18Pj",
        "outputId": "8fd04a80-ccbf-4f6c-df68-3170e7b94b68"
      },
      "id": "N7pogvfu18Pj",
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "a1ac5ed6",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a1ac5ed6",
        "outputId": "988e0b21-4d64-4440-8d8f-a32c0572f3a2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index(['id', 'type', 'name', 'yearpublished', 'minplayers', 'maxplayers',\n",
            "       'playingtime', 'minplaytime', 'maxplaytime', 'minage', 'users_rated',\n",
            "       'average_rating', 'bayes_average_rating', 'total_owners',\n",
            "       'total_traders', 'total_wanters', 'total_wishers', 'total_comments',\n",
            "       'total_weights', 'average_weight'],\n",
            "      dtype='object')\n",
            "(81312, 20)\n"
          ]
        }
      ],
      "source": [
        "#read csv data\n",
        "games = pandas.read_csv(\"/content/drive/MyDrive/Colab Notebooks/games.csv\")\n",
        "#print columns of csv and number of entries\n",
        "print(games.columns)\n",
        "print(games.shape)"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "PUM8Pdp616R4"
      },
      "id": "PUM8Pdp616R4",
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "cb50e8de",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "cb50e8de",
        "outputId": "c55c3cc4-d7b0-4fc4-fc0d-ad3554f66494"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Make a histogram of average rating column\n",
        "plt.hist(games[\"average_rating\"])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "87f4d75e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "87f4d75e",
        "outputId": "844287b6-d172-4826-8ea2-92bb95d2559b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "id                             318\n",
            "type                     boardgame\n",
            "name                    Looney Leo\n",
            "yearpublished                  0.0\n",
            "minplayers                     0.0\n",
            "maxplayers                     0.0\n",
            "playingtime                    0.0\n",
            "minplaytime                    0.0\n",
            "maxplaytime                    0.0\n",
            "minage                         0.0\n",
            "users_rated                      0\n",
            "average_rating                 0.0\n",
            "bayes_average_rating           0.0\n",
            "total_owners                     0\n",
            "total_traders                    0\n",
            "total_wanters                    0\n",
            "total_wishers                    1\n",
            "total_comments                   0\n",
            "total_weights                    0\n",
            "average_weight                 0.0\n",
            "Name: 13048, dtype: object\n",
            "=======================================\n",
            "id                                  12333\n",
            "type                            boardgame\n",
            "name                    Twilight Struggle\n",
            "yearpublished                      2005.0\n",
            "minplayers                            2.0\n",
            "maxplayers                            2.0\n",
            "playingtime                         180.0\n",
            "minplaytime                         180.0\n",
            "maxplaytime                         180.0\n",
            "minage                               13.0\n",
            "users_rated                         20113\n",
            "average_rating                    8.33774\n",
            "bayes_average_rating              8.22186\n",
            "total_owners                        26647\n",
            "total_traders                         372\n",
            "total_wanters                        1219\n",
            "total_wishers                        5865\n",
            "total_comments                       5347\n",
            "total_weights                        2562\n",
            "average_weight                     3.4785\n",
            "Name: 0, dtype: object\n"
          ]
        }
      ],
      "source": [
        "#print first row of games with 0 average scores\n",
        "print(games[games[\"average_rating\"] == 0].iloc[0])\n",
        "print(\"=======================================\")\n",
        "print(games[games[\"average_rating\"] > 0].iloc[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "2e2bad92",
      "metadata": {
        "id": "2e2bad92"
      },
      "outputs": [],
      "source": [
        "#remove rows with no user reviews\n",
        "games = games[games[\"users_rated\"] > 0]\n",
        "\n",
        "#remove rows with missing values\n",
        "games = games.dropna(axis=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "1b8e3a63",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "1b8e3a63",
        "outputId": "ca1bc26b-6503-487a-8f71-2701f9cb5fa2"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Make a histogram of average rating column\n",
        "plt.hist(games[\"average_rating\"])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "bec8249e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 804
        },
        "id": "bec8249e",
        "outputId": "79c02caf-2aae-4813-9e4b-f1e6a22cf89a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x864 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# correlation matrix\n",
        "corrmat = games.corr()\n",
        "fig = plt.figure(figsize = (15, 12))\n",
        "sns.heatmap(corrmat, vmax = 1, square = True)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "2f3c5765",
      "metadata": {
        "id": "2f3c5765"
      },
      "outputs": [],
      "source": [
        "#Get columns from dataframe\n",
        "columns = games.columns.tolist()\n",
        "\n",
        "# remove columns we don't want\n",
        "\n",
        "columns = [c for c in columns if c not in [\"bayes_average_rating\", \"average_rating\", \"type\", \"name\", \"id\"]]\n",
        "\n",
        "target = \"average_rating\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "d3282599",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d3282599",
        "outputId": "2be92d4f-f1b3-45a2-8d94-2330f4bc90bf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train shape\n",
            "(45515, 20)\n",
            "===================================\n",
            "Test shape\n",
            "(11379, 20)\n"
          ]
        }
      ],
      "source": [
        "# split training and test dataset\n",
        "\n",
        "train = games.sample(frac=0.8, random_state = 1)\n",
        "test = games.loc[~games.index.isin(train.index)]\n",
        "\n",
        "print(\"Train shape\")\n",
        "print(train.shape)\n",
        "print(\"===================================\")\n",
        "print(\"Test shape\")\n",
        "print(test.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "3f1b4cc3",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3f1b4cc3",
        "outputId": "4a3dbde8-16c4-429a-ff1b-f3ff9097b186"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "# Linear regression model\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import mean_squared_error\n",
        "\n",
        "#init the model\n",
        "LR = LinearRegression()\n",
        "\n",
        "#fit model to data\n",
        "\n",
        "LR.fit(train[columns], train[target])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "29097390",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "29097390",
        "outputId": "bbf97b95-62eb-44a4-8d9a-8c2839a90213"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.0788190326293234"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "#Generate the predictions\n",
        "predictions = LR.predict(test[columns])\n",
        "\n",
        "# calc error between prediction and actual values\n",
        "\n",
        "mean_squared_error(predictions,test[target])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "id": "a38b1c63",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a38b1c63",
        "outputId": "455cb11a-f8ab-4812-f4ef-6e43bea77423"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor(min_samples_leaf=10, random_state=1)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "# Non-linear model\n",
        "\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "\n",
        "#init the model \n",
        "\n",
        "RFR = RandomForestRegressor(n_estimators = 100, min_samples_leaf = 10, random_state = 1)\n",
        "\n",
        "#fit model to data\n",
        "RFR.fit(train[columns], train[target])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "id": "54d0e478",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "54d0e478",
        "outputId": "977f9986-1a83-44cd-ce75-146044d08f19"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.4458625791207198"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "# Generate the predictions\n",
        "predictions = RFR.predict(test[columns])\n",
        "\n",
        "# calc error between prediction and actual values\n",
        "\n",
        "mean_squared_error(predictions,test[target])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "id": "eb105a20",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eb105a20",
        "outputId": "e705f527-e9a0-4b8f-9863-1493f53d1474"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "yearpublished      2011.0000\n",
              "minplayers            2.0000\n",
              "maxplayers            6.0000\n",
              "playingtime         200.0000\n",
              "minplaytime          60.0000\n",
              "maxplaytime         200.0000\n",
              "minage               14.0000\n",
              "users_rated       15709.0000\n",
              "total_owners      17611.0000\n",
              "total_traders       273.0000\n",
              "total_wanters      1108.0000\n",
              "total_wishers      5581.0000\n",
              "total_comments     3188.0000\n",
              "total_weights      1486.0000\n",
              "average_weight        3.6359\n",
              "Name: 9, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "test[columns].iloc[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "id": "5af220f8",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5af220f8",
        "outputId": "b61b5a27-c993-49fb-dbf9-f42838cb9a82"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LR rating :   [8.12061283]\n",
            "LR rating :   [7.91373581]\n",
            "Actual rating :   8.07933\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/base.py:451: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
            "  \"X does not have valid feature names, but\"\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/base.py:451: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
            "  \"X does not have valid feature names, but\"\n"
          ]
        }
      ],
      "source": [
        "rating_LR = LR.predict(test[columns].iloc[0].values.reshape(1,-1))\n",
        "rating_RFR = RFR.predict(test[columns].iloc[0].values.reshape(1,-1))\n",
        "\n",
        "print(\"LR rating :  \", rating_LR)\n",
        "print(\"LR rating :  \", rating_RFR)\n",
        "\n",
        "print(\"Actual rating :  \", test[target].iloc[0])\n"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.12"
    },
    "colab": {
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}