LOL_game_result_prediction_basic.ipynb

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      "metadata": {
        "id": "hTZS7jQG1311"
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      "source": [
        "## LOL Outcome Prediction ##\n"
      ]
    },
    {
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              "// you may not use this file except in compliance with the License.\n",
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              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
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              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
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              "  };\n",
              "\n",
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              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
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              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
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              "          100 :\n",
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              "\n",
              "scope.google = scope.google || {};\n",
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            ]
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving lol.csv to lol.csv\n"
          ]
        }
      ],
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "CYg0O0qJLcxg",
        "outputId": "0bb868b5-0932-4770-bfb4-8fef6ddaa8c2"
      },
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              "          gameId  blueWins  blueWardsPlaced  blueWardsDestroyed  \\\n",
              "0     4519157822         0               28                   2   \n",
              "1     4523371949         0               12                   1   \n",
              "2     4521474530         0               15                   0   \n",
              "3     4524384067         0               43                   1   \n",
              "4     4436033771         0               75                   4   \n",
              "...          ...       ...              ...                 ...   \n",
              "9874  4527873286         1               17                   2   \n",
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              "9876  4527713716         0               23                   1   \n",
              "9877  4527628313         0               14                   4   \n",
              "9878  4523772935         1               18                   0   \n",
              "\n",
              "      blueFirstBlood  blueKills  blueDeaths  blueAssists  blueEliteMonsters  \\\n",
              "0                  1          9           6           11                  0   \n",
              "1                  0          5           5            5                  0   \n",
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              "9877               1          2           3            3                  1   \n",
              "9878               1          6           6            5                  0   \n",
              "\n",
              "      blueDragons  ...  redTowersDestroyed  redTotalGold  redAvgLevel  \\\n",
              "0               0  ...                   0         16567          6.8   \n",
              "1               0  ...                   1         17620          6.8   \n",
              "2               1  ...                   0         17285          6.8   \n",
              "3               0  ...                   0         16478          7.0   \n",
              "4               0  ...                   0         17404          7.0   \n",
              "...           ...  ...                 ...           ...          ...   \n",
              "9874            1  ...                   0         15246          6.8   \n",
              "9875            1  ...                   0         15456          7.0   \n",
              "9876            0  ...                   0         18319          7.4   \n",
              "9877            1  ...                   0         15298          7.2   \n",
              "9878            0  ...                   0         15339          6.8   \n",
              "\n",
              "      redTotalExperience  redTotalMinionsKilled  redTotalJungleMinionsKilled  \\\n",
              "0                  17047                    197                           55   \n",
              "1                  17438                    240                           52   \n",
              "2                  17254                    203                           28   \n",
              "3                  17961                    235                           47   \n",
              "4                  18313                    225                           67   \n",
              "...                  ...                    ...                          ...   \n",
              "9874               16498                    229                           34   \n",
              "9875               18367                    206                           56   \n",
              "9876               19909                    261                           60   \n",
              "9877               18314                    247                           40   \n",
              "9878               17379                    201                           46   \n",
              "\n",
              "      redGoldDiff  redExperienceDiff  redCSPerMin  redGoldPerMin  \n",
              "0            -643                  8         19.7         1656.7  \n",
              "1            2908               1173         24.0         1762.0  \n",
              "2            1172               1033         20.3         1728.5  \n",
              "3            1321                  7         23.5         1647.8  \n",
              "4            1004               -230         22.5         1740.4  \n",
              "...           ...                ...          ...            ...  \n",
              "9874        -2519              -2469         22.9         1524.6  \n",
              "9875         -782               -888         20.6         1545.6  \n",
              "9876         2416               1877         26.1         1831.9  \n",
              "9877          839               1085         24.7         1529.8  \n",
              "9878         -927                 58         20.1         1533.9  \n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>gameId</th>\n",
              "      <th>blueWins</th>\n",
              "      <th>blueWardsPlaced</th>\n",
              "      <th>blueWardsDestroyed</th>\n",
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              "    <tr>\n",
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              "      <td>4523371949</td>\n",
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              "      <td>17285</td>\n",
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              "      <td>17254</td>\n",
              "      <td>203</td>\n",
              "      <td>28</td>\n",
              "      <td>1172</td>\n",
              "      <td>1033</td>\n",
              "      <td>20.3</td>\n",
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              "      <td>1831.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9877</th>\n",
              "      <td>4527628313</td>\n",
              "      <td>0</td>\n",
              "      <td>14</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>15298</td>\n",
              "      <td>7.2</td>\n",
              "      <td>18314</td>\n",
              "      <td>247</td>\n",
              "      <td>40</td>\n",
              "      <td>839</td>\n",
              "      <td>1085</td>\n",
              "      <td>24.7</td>\n",
              "      <td>1529.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9878</th>\n",
              "      <td>4523772935</td>\n",
              "      <td>1</td>\n",
              "      <td>18</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>15339</td>\n",
              "      <td>6.8</td>\n",
              "      <td>17379</td>\n",
              "      <td>201</td>\n",
              "      <td>46</td>\n",
              "      <td>-927</td>\n",
              "      <td>58</td>\n",
              "      <td>20.1</td>\n",
              "      <td>1533.9</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>9879 rows × 40 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-628a3443-936c-431c-8ca3-92c9285b5b41')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "          const element = document.querySelector('#df-628a3443-936c-431c-8ca3-92c9285b5b41');\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import keras\n",
        "import sklearn\n",
        "\n",
        "from keras.utils import to_categorical\n",
        "import sklearn.preprocessing\n",
        "from sklearn import metrics\n",
        "from sklearn.model_selection import train_test_split\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "\n",
        "lol=pd.read_csv('lol.csv')\n",
        "lol"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YoGQSnSgL952"
      },
      "outputs": [],
      "source": [
        "x=lol\n",
        "x=x.drop(['gameId','blueWins'],axis=1)\n",
        "y=lol['blueWins']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "J-p036QSMDk9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f244c24e-32b2-40a2-e237-c5a5d231c05b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/optimizers/legacy/adam.py:117: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "  super().__init__(name, **kwargs)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "247/247 [==============================] - 1s 3ms/step - loss: 283.8900 - Accuracy: 0.6342\n",
            "Epoch 2/10\n",
            "247/247 [==============================] - 1s 3ms/step - loss: 12.8447 - Accuracy: 0.6339\n",
            "Epoch 3/10\n",
            "247/247 [==============================] - 1s 4ms/step - loss: 1.9166 - Accuracy: 0.6452\n",
            "Epoch 4/10\n",
            "247/247 [==============================] - 1s 3ms/step - loss: 0.8640 - Accuracy: 0.6778\n",
            "Epoch 5/10\n",
            "247/247 [==============================] - 1s 2ms/step - loss: 0.5687 - Accuracy: 0.7090\n",
            "Epoch 6/10\n",
            "247/247 [==============================] - 1s 2ms/step - loss: 0.5574 - Accuracy: 0.7133\n",
            "Epoch 7/10\n",
            "247/247 [==============================] - 1s 2ms/step - loss: 0.5562 - Accuracy: 0.7118\n",
            "Epoch 8/10\n",
            "247/247 [==============================] - 1s 2ms/step - loss: 0.5512 - Accuracy: 0.7206\n",
            "Epoch 9/10\n",
            "247/247 [==============================] - 1s 2ms/step - loss: 0.5525 - Accuracy: 0.7162\n",
            "Epoch 10/10\n",
            "247/247 [==============================] - 1s 2ms/step - loss: 0.5524 - Accuracy: 0.7220\n"
          ]
        }
      ],
      "source": [
        "import keras\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2)\n",
        "\n",
        "net = keras.models.Sequential()\n",
        "net.add(keras.layers.Dense(300, activation=\"relu\", input_shape=(38,)))\n",
        "net.add(keras.layers.Dense(2, activation='softmax'))\n",
        "\n",
        "from keras.optimizers import Adam\n",
        "Adam=Adam(lr=0.01)\n",
        "\n",
        "net.compile(loss='categorical_crossentropy', optimizer=Adam, metrics=['Accuracy'])\n",
        "\n",
        "\n",
        "history = net.fit(x_train, to_categorical(y_train), epochs=10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "esrjCuYxME-e",
        "outputId": "a0be7b27-012c-4bd0-a892-48693dc05552"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "62/62 [==============================] - 0s 2ms/step - loss: 0.5361 - Accuracy: 0.7287\n",
            "0.7287449240684509\n"
          ]
        }
      ],
      "source": [
        "test_loss, test_acc=net.evaluate(x_test, to_categorical(y_test))\n",
        "print(test_acc)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## some notes:\n",
        "\n",
        "feature selection: all colomns except gameID(irrelavant) and blueWins\n",
        "\n",
        "acc = num of correct predictions / total num of predictions\n",
        "\n",
        "\n",
        "3 layers;\n",
        "neurons 100 100 100;\n",
        "atvfunc = relu relu relu;\n",
        "acc = 0.50;\n",
        "\n",
        "\n",
        "5 layers;\n",
        "neurons 200 200 200 200 200;\n",
        "atvfunc = relu relu relu relu relu;\n",
        "acc = 0.50;\n",
        "\n",
        "\n",
        "10 layers;\n",
        "neurons all==400;\n",
        "atvfunc = all = relu;\n",
        "acc = 0.48;\n",
        "\n",
        "\n",
        "3 layers;\n",
        "neurons 100 100 100;\n",
        "atvfunc = linear sigmoid tanh;\n",
        "acc = 0.49;\n",
        "\n",
        "\n",
        "3 layers;\n",
        "neurons 500 500 500;\n",
        "atvfunc = relu;\n",
        "acc = 0.56;\n",
        "\n",
        "\n",
        "optimizers of above are all Adam with lr = 0.01;\n",
        "lossfunc of above are all categorical_crossentropy;\n",
        "\n",
        "\n",
        "change Adam lr to 0.001,\n",
        "acc became 0.50,\n",
        "change it back;\n",
        "\n",
        "5 layers;\n",
        "neurons 500 500 500 500 500;\n",
        "atvfunc = relu;\n",
        "acc = 0.49;\n",
        "\n",
        "\n",
        "#############################################################\n",
        "\n",
        "\n",
        "1 layer;\n",
        "neurons 500;\n",
        "atvfunc = relu;\n",
        "acc = 0.7140688300132751;\n",
        "\n",
        "\n",
        "1 layer;\n",
        "neurons 300;\n",
        "atvfunc = relu;\n",
        "acc = 0.7317813634872437;\n",
        "(shown in screenshot below)"
      ],
      "metadata": {
        "id": "AK2UTCEa94yQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        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