randomforest (1).ipynb

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    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from plotly.subplots import make_subplots\n",
        "import plotly.graph_objs as go\n"
      ],
      "metadata": {
        "id": "fEZgRFpmvHVZ"
      },
      "execution_count": null,
      "outputs": []
    },
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      "cell_type": "code",
      "source": [
        "\n",
        "historic_data = pd.read_csv('historic.csv')\n",
        "\n",
        "prediction_input = pd.read_csv('prediction_input.csv')\n",
        "\n",
        "encoder = LabelEncoder()\n",
        "historic_data['category'] = encoder.fit_transform(historic_data['category'])\n",
        "historic_data['main_promotion'] = encoder.fit_transform(historic_data['main_promotion'])\n",
        "historic_data['color'] = encoder.fit_transform(historic_data['color'])\n",
        "\n",
        "X = historic_data.drop(['item_no', 'success_indicator'], axis=1)\n",
        "y = historic_data['success_indicator']\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n"
      ],
      "metadata": {
        "id": "WbNYC0BPvJso"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "rf_model = RandomForestClassifier(n_estimators=100, random_state=42)\n",
        "\n"
      ],
      "metadata": {
        "id": "1h3m0tYnvNkN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "print(historic_data.describe())\n"
      ],
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        "id": "Z9Hwq2s2vTC2",
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          "text": [
            "             item_no        stars\n",
            "count    8000.000000  8000.000000\n",
            "mean   546474.338375     3.473075\n",
            "std    256513.463874     0.978810\n",
            "min    100171.000000     0.300000\n",
            "25%    325001.500000     2.800000\n",
            "50%    547081.000000     3.500000\n",
            "75%    764312.250000     4.200000\n",
            "max    989740.000000     6.100000\n"
          ]
        }
      ]
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      "cell_type": "code",
      "source": [
        "\n",
        "rf_model.fit(X_train, y_train)\n",
        "\n"
      ],
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              "RandomForestClassifier(random_state=42)"
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            ]
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          "execution_count": 48
        }
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    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = rf_model.predict(X_test)\n",
        "\n",
        "\n",
        "print(classification_report(y_test, y_pred))\n",
        "\n",
        "\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "print(\"Confusion Matrix:\\n\", cm)\n"
      ],
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          "base_uri": "https://localhost:8080/"
        },
        "id": "CFTLms5bveRx",
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      "execution_count": null,
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          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "        flop       0.80      0.68      0.73       571\n",
            "         top       0.84      0.90      0.87      1029\n",
            "\n",
            "    accuracy                           0.82      1600\n",
            "   macro avg       0.82      0.79      0.80      1600\n",
            "weighted avg       0.82      0.82      0.82      1600\n",
            "\n",
            "Confusion Matrix:\n",
            " [[388 183]\n",
            " [100 929]]\n"
          ]
        }
      ]
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        "feature_importances = rf_model.feature_importances_\n",
        "\n",
        "feature_importances_df = pd.DataFrame({\n",
        "    'Feature': X_train.columns,\n",
        "    'Importance': feature_importances\n",
        "}).sort_values('Importance', ascending=False)\n",
        "\n",
        "\n",
        "fig = go.Figure(go.Bar(\n",
        "    x=feature_importances_df['Feature'],\n",
        "    y=feature_importances_df['Importance']\n",
        "))\n",
        "fig.update_layout(title='Feature Importances', xaxis_title='Feature', yaxis_title='Importance')\n",
        "fig.show()\n"
      ],
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              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "import joblib\n",
        "joblib.dump(rf_model, 'random_forest_model.pkl')\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8UbngWGVvidZ",
        "outputId": "fb4596ef-8c06-4435-eaf4-6c2063ff2136"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['random_forest_model.pkl']"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Histogram of star ratings\n",
        "fig = px.histogram(historic_data, x='stars', nbins=5, title='Distribution of Star Ratings')\n",
        "fig.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "2NwBrZL1wNWc",
        "outputId": "f494dec1-0244-4822-fc41-9197b8cbb801"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Display the first few rows of the dataset\n",
        "print(historic_data.head())\n",
        "\n",
        "# Display the last few rows of the dataset\n",
        "print(historic_data.tail())\n",
        "\n",
        "# Show the shape of the dataframe\n",
        "print(historic_data.shape)\n",
        "\n",
        "# Display general information about the dataframe\n",
        "print(historic_data.info())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5DMzWHoDvjrR",
        "outputId": "ff789ecb-43f3-45bb-9db4-2bce0f4515be"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   item_no  category  main_promotion  color  stars success_indicator\n",
            "0   739157         5               0      3    3.1              flop\n",
            "1   591846         1               1      7    1.5              flop\n",
            "2   337574         3               0      7    4.4               top\n",
            "3   401933         2               1      1    3.1              flop\n",
            "4   812151         1               1      3    4.1               top\n",
            "      item_no  category  main_promotion  color  stars success_indicator\n",
            "7995   280947         4               0      1    3.9              flop\n",
            "7996   874952         3               3      3    3.5               top\n",
            "7997   891640         4               1      9    4.0              flop\n",
            "7998   786448         0               1      1    2.6              flop\n",
            "7999   847223         3               2      1    3.9               top\n",
            "(8000, 6)\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 8000 entries, 0 to 7999\n",
            "Data columns (total 6 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   item_no            8000 non-null   int64  \n",
            " 1   category           8000 non-null   int64  \n",
            " 2   main_promotion     8000 non-null   int64  \n",
            " 3   color              8000 non-null   int64  \n",
            " 4   stars              8000 non-null   float64\n",
            " 5   success_indicator  8000 non-null   object \n",
            "dtypes: float64(1), int64(4), object(1)\n",
            "memory usage: 375.1+ KB\n",
            "None\n"
          ]
        }
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    {
      "cell_type": "code",
      "source": [
        "# Display the count of unique values in each categorical column\n",
        "for col in ['category', 'main_promotion', 'color']:\n",
        "    print(historic_data[col].value_counts())\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vMhdAhemwRum",
        "outputId": "ee16be50-e126-4dc0-d5e2-32f2a5f93cd0"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5    1650\n",
            "2    1546\n",
            "4    1459\n",
            "3    1360\n",
            "0    1246\n",
            "1     739\n",
            "Name: category, dtype: int64\n",
            "1    2432\n",
            "0    2246\n",
            "3    2013\n",
            "2    1309\n",
            "Name: main_promotion, dtype: int64\n",
            "4    1443\n",
            "1    1244\n",
            "9    1056\n",
            "0     812\n",
            "7     776\n",
            "3     728\n",
            "5     592\n",
            "2     585\n",
            "6     412\n",
            "8     352\n",
            "Name: color, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Histogram of star ratings\n",
        "fig = px.histogram(historic_data, x='stars', nbins=5, title='Distribution of Star Ratings')\n",
        "fig.show()\n"
      ],
      "metadata": {
        "id": "iElNnc9Jzve0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "c0348e11-fcda-4809-c6a9-9249be5e2e06"
      },
      "execution_count": 54,
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      "source": [
        "# Correlation heatmap of numeric variables\n",
        "numeric_features = ['stars']  # Add other numeric features if needed\n",
        "correlation_matrix = historic_data[numeric_features].corr().round(2)\n",
        "fig = px.imshow(correlation_matrix, labels={'x': 'Features', 'y': 'Features'}, title='Correlation Heatmap')\n",
        "fig.show()\n"
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      "metadata": {
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          "height": 542
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      },
      "execution_count": 55,
      "outputs": [
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    {
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      "source": [
        "# Box plot for outlier detection\n",
        "fig = px.box(historic_data, y='stars', title='Box Plot of Star Ratings')\n",
        "fig.show()\n"
      ],
      "metadata": {
        "id": "aGGdrc9Iz0Z4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
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        "outputId": "92fa3c9a-f8ed-427b-c080-56b3d04cc196"
      },
      "execution_count": 56,
      "outputs": [
        {
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4,3.5,3.3,2.7,2.3,2.3,2.9,5.4,3.2,4.2,4.5,5.1,2.4,4.0,4.5,2.9,3.1,2.2,2.5,3.7,3.6,2.4,4.0,2.0,2.6,5.1,3.4,3.5,2.4,2.6,3.9,4.2,5.0,3.4,3.3,3.1,2.3,3.5,2.0,3.5,4.1,4.3,3.5,1.8,4.1,3.4,3.2,4.6,2.7,2.8,4.8,4.1,4.0,3.5,4.3,2.1,3.1,3.1,3.6,4.7,3.7,3.7,4.5,2.9,2.9,5.3,2.4,4.5,3.1,3.1,4.8,3.8,3.4,3.1,5.6,5.1,4.9,2.8,2.7,3.3,3.3,4.2,3.9,3.8,1.4,2.3,2.9,2.2,2.4,2.6,2.7,4.1,2.0,3.3,4.7,2.6,3.1,1.3,2.4,3.7,2.2,3.4,4.6,1.5,3.7,2.2,3.5,2.1,4.5,3.9,2.6,3.7,2.8,5.3,3.1,2.3,3.2,3.1,5.4,4.6,1.4,1.0,2.4,4.8,4.5,3.9,2.9,1.4,2.9,3.8,4.9,5.4,3.0,3.1,3.4,2.8,4.0,1.7,0.8,5.4,3.1,3.6,3.4,3.1,2.8,5.4,4.5,4.0,3.2,4.0,3.3,5.1,3.3,1.8,4.2,3.9,3.1,2.2,4.7,4.2,0.9,4.4,3.2,3.9,3.5,1.2,2.9,4.2,3.2,3.0,4.7,4.5,3.1,3.5,2.4,4.6,4.1,3.6,4.8,4.9,3.8,4.5,3.5,3.0,4.0,5.5,3.0,4.1,4.1,2.9,3.3,2.9,4.0,3.0,5.3,1.8,3.7,4.8,2.3,2.0,1.7,3.3,3.4,4.5,3.4,4.4,2.7,4.3,4.2,4.6,2.5,2.7,4.6,4.3,4.8,4.9,4.1,2.7,3.1,1.1,4.0,3.8,2.8,5.0,4.8,2.8,3.7,3.5,5.0,3.3,3.8,3.0,3.6,4.7,5.2,4.7,3.0,2.4,3.3,3.1,3.9,5.2,4.6,4.8,3.3,1.5,1.0,2.9,3.4,5.1,3.1,3.4,4.4,2.6,2.2,3.7,3.2,2.9,4.2,3.6,3.0,3.4,2.5,3.8,4.8,4.1,1.9,5.0,4.0,2.8,4.2,2.7,2.6,4.6,3.6,4.6,4.7,5.0,5.1,5.3,4.5,4.8,2.8,3.0,4.6,4.7,3.7,2.0,2.6,2.3,4.3,3.2,3.2,3.9,3.5,3.1,3.7,5.0,1.4,2.9,4.0,2.7,2.7,5.1,5.0,2.1,4.5,4.0,5.1,4.5,3.4,3.6,1.9,2.4,4.5,4.7,3.6,2.5,1.6,4.1,5.1,3.1,2.8,4.4,3.5,3.3,3.4,4.3,4.7,3.8,3.6,3.9,3.8,3.4,3.3,5.0,2.5,3.2,2.2,3.4,3.9,3.6,1.4,4.2,3.1,3.7,4.7,3.9,2.8,3.5,3.6,4.6,5.2,4.0,3.5,3.9,5.6,3.6,3.5,1.9,3.0,3.0,4.1,3.7,4.0,4.6,4.9,3.9,3.9,2.6,4.6,3.6,3.0,3.9,3.5,2.1,3.1,5.2,2.6,4.7,3.6,3.8,4.8,4.2,3.1,2.9,2.9,5.1,3.9,4.4,3.1,4.6,1.3,4.9,3.1,3.0,1.8,4.4,1.8,4.2,4.0,1.9,4.9,2.3,1.7,2.8,4.6,2.8,1.8,5.3,4.6,3.9,3.4,4.4,5.2,4.0,4.9,3.2,5.1,3.5,4.9,4.3,3.8,2.5,2.2,4.8,4.4,3.6,3.4,2.9,3.3,4.2,5.0,3.5,3.9,3.4,4.1,3.7,4.6,3.6,3.6,4.5,3.9,2.7,3.8,4.4,4.8,3.4,1.2,4.0,3.9,3.6,4.4,3.8,1.4,3.3,2.4,3.7,4.8,2.0,2.5,5.4,3.6,2.7,2.6,5.5,3.6,5.0,5.2,3.6,3.9,4.0,4.9,3.3,3.5,2.3,3.9,2.8,3.1,4.8,2.5,1.4,4.0,3.3,4.5,4.6,3.5,4.8,2.1,4.9,3.5,3.1,4.2,3.7,5.4,2.9,4.1,3.1,3.7,2.5,2.4,4.9,4.6,2.7,3.1,3.0,4.8,4.1,2.5,2.3,4.1,5.9,4.3,2.7,4.0,5.3,4.9,3.3,2.9,1.6,3.1,1.7,3.6,2.8,5.6,2.9,2.6,2.5,2.5,3.8,2.0,4.7,2.0,4.1,3.8,4.6,2.9,4.2,0.9,4.7,2.2,3.3,2.4,3.4,4.4,3.7,4.6,3.2,3.6,2.8,2.7,2.1,3.2,4.7,3.7,5.2,3.5,4.7,2.9,4.1,3.0,5.0,2.7,3.6,2.7,2.4,3.9,4.5,3.7,4.6,3.4,4.8,5.0,4.8,4.6,2.5,3.4,4.0,1.5,2.3,3.9,4.1,1.5,4.7,3.9,4.7,1.7,4.1,3.3,5.0,5.1,2.4,3.2,1.5,3.7,3.0,3.0,3.2,3.1,3.0,4.0,4.7,2.6,3.8,4.9,2.9,3.7,3.6,3.6,1.9,5.1,1.0,3.9,4.0,3.1,3.1,2.9,4.9,1.4,4.8,2.2,2.5,2.7,2.0,3.6,4.9,3.2,4.1,4.5,3.2,3.1,4.0,1.6,2.2,3.5,4.3,3.0,5.3,3.3,2.2,2.2,1.9,4.0,4.3,2.9,3.0,2.4,4.3,3.0,5.0,3.8,3.1,3.6,2.3,4.6,3.1,3.2,3.2,4.0,3.7,3.5,2.6,4.8,3.8,1.5,5.0,2.4,2.5,3.5,3.2,3.4,1.1,3.0,2.3,4.4,5.5,5.0,4.0,5.0,4.0,2.8,2.5,4.8,3.5,4.3,3.8,4.8,3.4,3.2,3.0,4.6,4.4,3.8,4.0,3.1,4.3,3.6,4.5,3.2,2.5,2.6,3.2,3.2,4.3,3.8,3.5,1.7,3.4,3.0,2.2,4.4,5.1,3.4,4.8,1.1,4.3,3.7,3.5,3.8,3.3,4.1,3.0,4.2,4.3,4.6,1.7,2.3,3.2,4.4,4.3,5.3,3.5,4.1,3.7,3.4,2.0,4.6,4.0,3.4,3.9,2.4,4.0,2.6,3.7,4.6,3.7,4.3,4.6,4.5,2.8,2.9,3.0,3.3,2.6,3.3,3.8,4.6,4.5,3.7,4.4,3.2,3.0,4.6,3.8,4.9,4.0,3.5,2.3,4.9,3.8,4.6,2.3,4.0,4.0,5.2,4.7,4.9,4.1,5.2,4.3,3.3,3.8,4.0,3.8,5.0,4.2,4.7,4.7,3.5,2.9,3.7,2.3,4.3,2.6,3.5,2.4,3.6,2.9,4.3,3.1,2.9,4.3,4.1,3.6,4.1,2.9,4.1,4.8,4.0,4.6,2.4,4.3,2.3,2.7,4.3,2.1,2.8,4.2,2.2,5.3,3.0,4.1,2.8,3.0,3.1,5.2,4.3,3.2,4.0,1.1,4.6,1.0,1.8,5.0,3.5,1.9,4.3,4.1,3.3,5.4,4.0,4.6,4.1,3.9,4.1,2.8,2.8,3.2,3.4,3.4,3.8,2.8,4.9,4.6,4.1,4.6,5.2,3.5,5.5,3.4,2.3,4.5,3.9,4.9,3.2,4.9,1.4,3.8,3.1,2.7,3.6,3.6,3.6,3.9,2.6,4.6,3.4,2.4,2.6,2.2,3.3,4.2,3.6,4.1,4.2,4.6,0.8,4.0,3.0,3.6,1.8,3.7,4.4,3.2,4.0,4.6,4.3,5.1,3.0,3.5,2.9,3.1,2.5,2.1,4.1,4.1,3.7,3.1,4.4,3.7,1.3,3.5,2.6,3.3,3.3,2.9,4.3,3.7,2.4,2.3,3.7,3.8,2.9,4.7,3.1,3.4,2.9,3.1,4.3,5.0,3.0,3.3,3.5,4.6,4.1,4.7,3.9,4.9,3.1,1.0,4.2,1.6,3.7,2.8,3.6,3.8,2.6,3.6,4.4,4.4,2.1,4.4,2.0,3.9,2.9,3.3,3.4,4.6,3.6,3.8,3.9,2.1,2.2,4.5,3.6,3.6,4.2,2.6,4.6,3.3,4.5,3.9,1.8,3.9,4.3,3.2,4.6,5.1,1.5,3.4,3.7,2.4,2.0,3.6,2.8,3.4,3.4,3.0,3.6,4.4,2.8,3.1,2.1,2.7,2.7,3.3,2.2,2.9,2.1,2.5,3.7,4.4,4.5,3.8,5.3,4.5,3.2,3.2,2.8,4.3,4.9,1.8,5.0,3.1,2.9,1.3,4.6,3.0,2.8,3.4,3.3,2.0,4.5,4.7,2.3,2.2,2.3,3.5,4.5,3.3,1.6,3.9,5.1,4.4,3.8,3.2,3.3,4.1,3.6,1.4,2.8,4.0,1.9,5.4,4.2,4.9,3.0,4.4,2.9,3.7,2.8,4.0,2.7,4.8,3.0,3.3,4.4,1.5,1.8,5.1,2.8,5.0,3.5,3.5,3.5,4.9,2.6,3.1,4.0,5.1,1.9,3.9,4.0,5.0,3.4,4.0,2.4,4.6,3.4,4.3,2.7,3.3,3.8,4.3,5.3,2.8,4.4,4.4,3.7,5.2,3.3,3.7,5.4,2.6,3.0,2.2,2.4,4.2,3.1,3.5,2.8,4.9,4.0,2.0,4.1,2.1,3.6,2.1,4.1,1.6,2.8,2.4,3.0,5.0,4.8,2.4,3.8,2.9,2.3,5.4,3.0,5.0,4.5,3.9,4.5,4.7,2.9,2.8,3.9,4.9,2.4,3.1,4.3,2.8,3.2,2.4,5.1,5.9,3.1,1.7,2.6,3.4,3.5,2.9,1.7,3.0,3.8,5.4,3.5,2.5,5.0,2.8,3.5,3.0,3.5,1.7,4.5,3.6,4.4,2.6,3.4,4.6,2.9,3.3,2.6,2.9,5.1,3.7,3.9,2.4,1.9,5.0,4.8,3.7,3.3,1.9,2.9,3.4,3.7,3.1,2.2,2.6,4.6,2.5,4.5,3.5,3.9,2.5,3.4,4.4,3.2,4.1,4.5,3.4,2.5,3.4,2.2,3.6,3.5,2.9,3.1,3.8,4.7,3.4,3.5,4.7,3.5,2.6,3.6,2.7,2.5,4.5,4.7,3.1,4.7,4.0,3.9,2.4,4.4,3.4,4.0,2.7,3.3,1.9,4.1,2.9,2.9,3.7,2.9,5.2,2.7,4.7,4.4,3.0,2.8,2.7,3.4,2.4,2.9,3.2,2.7,2.9,3.8,4.6,4.5,2.6,3.9,5.7,3.5,1.8,2.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