classification_exotic_fruits.ipynb

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      "source": [
        "import pandas as pd\n",
        "\n",
        "df = pd.read_csv(\"https://proai-datasets.s3.eu-west-3.amazonaws.com/fruits.csv\")\n",
        "print(df.shape)\n",
        "print(df.describe())\n",
        "print(df.info())\n",
        "df.head()"
      ],
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          "text": [
            "(500, 6)\n",
            "         Peso (g)  Diametro medio (mm)  Lunghezza media (mm)  \\\n",
            "count  500.000000           500.000000            500.000000   \n",
            "mean    47.313440            86.573100              5.298620   \n",
            "std     26.768797            64.293403              2.641993   \n",
            "min      8.570000             7.530000              1.150000   \n",
            "25%     24.797500            50.510000              2.672500   \n",
            "50%     42.380000            70.450000              5.670000   \n",
            "75%     68.080000            88.852500              7.455000   \n",
            "max    111.210000           299.890000             11.140000   \n",
            "\n",
            "       Durezza buccia (1-10)  Dolcezza (1-10)  \n",
            "count             500.000000       500.000000  \n",
            "mean                7.437640         3.593220  \n",
            "std                 1.812548         1.264899  \n",
            "min                 3.070000         1.250000  \n",
            "25%                 6.152500         2.570000  \n",
            "50%                 7.340000         3.535000  \n",
            "75%                 8.615000         4.465000  \n",
            "max                13.720000         6.950000  \n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 500 entries, 0 to 499\n",
            "Data columns (total 6 columns):\n",
            " #   Column                 Non-Null Count  Dtype  \n",
            "---  ------                 --------------  -----  \n",
            " 0   Frutto                 500 non-null    object \n",
            " 1   Peso (g)               500 non-null    float64\n",
            " 2   Diametro medio (mm)    500 non-null    float64\n",
            " 3   Lunghezza media (mm)   500 non-null    float64\n",
            " 4   Durezza buccia (1-10)  500 non-null    float64\n",
            " 5   Dolcezza (1-10)        500 non-null    float64\n",
            "dtypes: float64(5), object(1)\n",
            "memory usage: 23.6+ KB\n",
            "None\n"
          ]
        },
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          "data": {
            "text/plain": [
              "  Frutto  Peso (g)  Diametro medio (mm)  Lunghezza media (mm)  \\\n",
              "0   Mela     86.40                89.68                  8.69   \n",
              "1   Mela     77.58                73.45                  6.49   \n",
              "2   Mela     81.95                81.66                  6.40   \n",
              "3   Mela     66.33                36.71                  6.78   \n",
              "4   Mela     56.73                75.69                  5.78   \n",
              "\n",
              "   Durezza buccia (1-10)  Dolcezza (1-10)  \n",
              "0                   9.61             2.41  \n",
              "1                   7.20             3.87  \n",
              "2                   9.09             2.88  \n",
              "3                   8.21             2.55  \n",
              "4                   9.15             3.88  "
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              "      <th></th>\n",
              "      <th>Frutto</th>\n",
              "      <th>Peso (g)</th>\n",
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              "      <td>Mela</td>\n",
              "      <td>86.40</td>\n",
              "      <td>89.68</td>\n",
              "      <td>8.69</td>\n",
              "      <td>9.61</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Mela</td>\n",
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              "      <td>9.09</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>6.78</td>\n",
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              "      <td>75.69</td>\n",
              "      <td>5.78</td>\n",
              "      <td>9.15</td>\n",
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 500,\n  \"fields\": [\n    {\n      \"column\": \"Frutto\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Banana\",\n          \"Kiwi\",\n          \"Arancia\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Peso (g)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 26.76879651198013,\n        \"min\": 8.57,\n        \"max\": 111.21,\n        \"num_unique_values\": 470,\n        \"samples\": [\n          60.98,\n          81.25,\n          101.38\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Diametro medio (mm)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 64.2934027772006,\n        \"min\": 7.53,\n        \"max\": 299.89,\n        \"num_unique_values\": 487,\n        \"samples\": [\n          69.07,\n          77.93,\n          59.02\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Lunghezza media (mm)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.6419930640144753,\n        \"min\": 1.15,\n        \"max\": 11.14,\n        \"num_unique_values\": 373,\n        \"samples\": [\n          6.61,\n          5.95,\n          6.48\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Durezza buccia (1-10)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.8125482317955488,\n        \"min\": 3.07,\n        \"max\": 13.72,\n        \"num_unique_values\": 349,\n        \"samples\": [\n          10.53,\n          6.71,\n          6.58\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Dolcezza (1-10)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.2648986397089197,\n        \"min\": 1.25,\n        \"max\": 6.95,\n        \"num_unique_values\": 305,\n        \"samples\": [\n          3.84,\n          5.37,\n          1.79\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          },
          "metadata": {},
          "execution_count": 1
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    {
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        "df.hist(figsize=(20,20))"
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<Axes: title={'center': 'Peso (g)'}>,\n",
              "        <Axes: title={'center': 'Diametro medio (mm)'}>],\n",
              "       [<Axes: title={'center': 'Lunghezza media (mm)'}>,\n",
              "        <Axes: title={'center': 'Durezza buccia (1-10)'}>],\n",
              "       [<Axes: title={'center': 'Dolcezza (1-10)'}>, <Axes: >]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 2
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x2000 with 6 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ho creato il dataframe dal file csv \"fruits.csv\". Analizzando il contenuto si può notare che non ci sono valori mancanti e i valori nelle rispettive colonne non necessitano di codifica."
      ],
      "metadata": {
        "id": "tHaeNbo28kx_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import Ridge, Lasso, ElasticNet\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
        "from matplotlib import pyplot as plt\n",
        "\n",
        "le = LabelEncoder()\n",
        "ss = StandardScaler()\n",
        "\n",
        "X = df.drop('Frutto', axis=1)\n",
        "y = le.fit_transform(df['Frutto'])\n",
        "plt.scatter(X[X.columns[0]], X[X.columns[1]], c=y)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "qxoZx7Pe9B73",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "outputId": "54935bd4-39de-4bd5-98ad-ce25a31d917e"
      },
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Visualizzazione distribuzione dati classificati"
      ],
      "metadata": {
        "id": "ri4PqsfAyCDB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.2)\n",
        "X_train = ss.fit_transform(X_train)\n",
        "X_test = ss.transform(X_test)"
      ],
      "metadata": {
        "id": "mleTouykx-lt"
      },
      "execution_count": 92,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Creazione del dataset di training e di test per addestrare i modelli."
      ],
      "metadata": {
        "id": "8UIiuk7AIBwM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.metrics import accuracy_score, log_loss\n",
        "\n",
        "for k in range(1, 20):\n",
        "    knn = KNeighborsClassifier(n_neighbors=k)\n",
        "    knn.fit(X_train, y_train)\n",
        "    y_pred_train = knn.predict(X_train)\n",
        "    y_proba_train = knn.predict_proba(X_train)\n",
        "    y_pred_test = knn.predict(X_test)\n",
        "    y_proba_test = knn.predict_proba(X_test)\n",
        "    print(f\"K: {k}\")\n",
        "    print(f\"accuracy train: {accuracy_score(y_train, y_pred_train):.3f}, accuracy test: {accuracy_score(y_test, y_pred_test):.3f}\")\n",
        "    print(f\"loss train: {log_loss(y_train, y_proba_train):.4f}, loss test: {log_loss(y_test, y_proba_test):.4f}\")\n",
        "    print(\"-----------------------------------------------\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lLIxTeAXIAsZ",
        "outputId": "e5489463-421f-4d9f-871a-6df8e69d9f61"
      },
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "K: 1\n",
            "accuracy train: 1.000, accuracy test: 0.890\n",
            "loss train: 0.0000, loss test: 3.9648\n",
            "-----------------------------------------------\n",
            "K: 2\n",
            "accuracy train: 0.950, accuracy test: 0.880\n",
            "loss train: 0.0658, loss test: 1.1853\n",
            "-----------------------------------------------\n",
            "K: 3\n",
            "accuracy train: 0.948, accuracy test: 0.900\n",
            "loss train: 0.1053, loss test: 0.5120\n",
            "-----------------------------------------------\n",
            "K: 4\n",
            "accuracy train: 0.938, accuracy test: 0.890\n",
            "loss train: 0.1239, loss test: 0.5059\n",
            "-----------------------------------------------\n",
            "K: 5\n",
            "accuracy train: 0.945, accuracy test: 0.910\n",
            "loss train: 0.1285, loss test: 0.1740\n",
            "-----------------------------------------------\n",
            "K: 6\n",
            "accuracy train: 0.932, accuracy test: 0.910\n",
            "loss train: 0.1400, loss test: 0.1646\n",
            "-----------------------------------------------\n",
            "K: 7\n",
            "accuracy train: 0.943, accuracy test: 0.930\n",
            "loss train: 0.1389, loss test: 0.1624\n",
            "-----------------------------------------------\n",
            "K: 8\n",
            "accuracy train: 0.935, accuracy test: 0.910\n",
            "loss train: 0.1448, loss test: 0.1644\n",
            "-----------------------------------------------\n",
            "K: 9\n",
            "accuracy train: 0.945, accuracy test: 0.910\n",
            "loss train: 0.1509, loss test: 0.1688\n",
            "-----------------------------------------------\n",
            "K: 10\n",
            "accuracy train: 0.932, accuracy test: 0.910\n",
            "loss train: 0.1537, loss test: 0.1624\n",
            "-----------------------------------------------\n",
            "K: 11\n",
            "accuracy train: 0.938, accuracy test: 0.910\n",
            "loss train: 0.1605, loss test: 0.1653\n",
            "-----------------------------------------------\n",
            "K: 12\n",
            "accuracy train: 0.932, accuracy test: 0.910\n",
            "loss train: 0.1609, loss test: 0.1579\n",
            "-----------------------------------------------\n",
            "K: 13\n",
            "accuracy train: 0.938, accuracy test: 0.930\n",
            "loss train: 0.1634, loss test: 0.1609\n",
            "-----------------------------------------------\n",
            "K: 14\n",
            "accuracy train: 0.938, accuracy test: 0.930\n",
            "loss train: 0.1654, loss test: 0.1595\n",
            "-----------------------------------------------\n",
            "K: 15\n",
            "accuracy train: 0.945, accuracy test: 0.940\n",
            "loss train: 0.1680, loss test: 0.1619\n",
            "-----------------------------------------------\n",
            "K: 16\n",
            "accuracy train: 0.935, accuracy test: 0.930\n",
            "loss train: 0.1714, loss test: 0.1646\n",
            "-----------------------------------------------\n",
            "K: 17\n",
            "accuracy train: 0.932, accuracy test: 0.940\n",
            "loss train: 0.1734, loss test: 0.1677\n",
            "-----------------------------------------------\n",
            "K: 18\n",
            "accuracy train: 0.940, accuracy test: 0.950\n",
            "loss train: 0.1741, loss test: 0.1661\n",
            "-----------------------------------------------\n",
            "K: 19\n",
            "accuracy train: 0.935, accuracy test: 0.940\n",
            "loss train: 0.1775, loss test: 0.1716\n",
            "-----------------------------------------------\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Creazione del modello KNN e visualizzazione delle metriche per k=1-20.\n",
        "Possiamo notare che il miglior parametro di k è **10**"
      ],
      "metadata": {
        "id": "2RutctpgVjXM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "knn = KNeighborsClassifier(n_neighbors=10)\n",
        "knn.fit(X_train, y_train)\n",
        "y_pred = knn.predict(X_test)\n",
        "\n",
        "plt.scatter(X_test[:,0], y_test, color='blue', label='Actual')\n",
        "plt.scatter(X_test[:,0], y_pred, color='red', label='Predicted')\n",
        "plt.title('KNN')\n",
        "plt.xlabel('Feature')\n",
        "plt.ylabel('Target')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "AppUgVZ3Wv1H",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "f5932814-da56-4201-cac2-7679e27f65ea"
      },
      "execution_count": 133,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Conclusioni\n",
        "Dal grafico in alto si può notare che il modello KNN è abbstanza generalista, quindi in grado di prevedere il frutto con un ottima accuratezza."
      ],
      "metadata": {
        "id": "RJ5muU8n3hP8"
      }
    }
  ]
}