AutoML.ipynb

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      "authorship_tag": "ABX9TyOFg50KDymxnB5GCVlxZetH",
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/GandharvaThite/Auto-ML/blob/main/AutoML.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "M15V4d3nlF9u"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/Copy of sonar data.csv',header = None)"
      ],
      "metadata": {
        "id": "25pG4IR-vcZX"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.drop(0)\n",
        "df"
      ],
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          "height": 424
        },
        "id": "RSt4rkf8vsBC",
        "outputId": "386473cc-51cc-45dd-9944-5a60736f73eb"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
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              "         0       1       2       3       4       5       6       7       8   \\\n",
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              "5    0.0286  0.0453  0.0277  0.0174  0.0384  0.0990  0.1201  0.1833  0.2105   \n",
              "..      ...     ...     ...     ...     ...     ...     ...     ...     ...   \n",
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              "207  0.0260  0.0363  0.0136  0.0272  0.0214  0.0338  0.0655  0.1400  0.1843   \n",
              "\n",
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              "         58      59  60  \n",
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              "5    0.0051  0.0062   R  \n",
              "..      ...     ...  ..  \n",
              "203  0.0193  0.0157   M  \n",
              "204  0.0062  0.0067   M  \n",
              "205  0.0077  0.0031   M  \n",
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              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
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              "      <th>60</th>\n",
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              "      <th>1</th>\n",
              "      <td>0.0453</td>\n",
              "      <td>0.0523</td>\n",
              "      <td>0.0843</td>\n",
              "      <td>0.0689</td>\n",
              "      <td>0.1183</td>\n",
              "      <td>0.2583</td>\n",
              "      <td>0.2156</td>\n",
              "      <td>0.3481</td>\n",
              "      <td>0.3337</td>\n",
              "      <td>0.2872</td>\n",
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              "      <td>0.0084</td>\n",
              "      <td>0.0089</td>\n",
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              "      <td>0.0094</td>\n",
              "      <td>0.0191</td>\n",
              "      <td>0.0140</td>\n",
              "      <td>0.0049</td>\n",
              "      <td>0.0052</td>\n",
              "      <td>0.0044</td>\n",
              "      <td>R</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.0262</td>\n",
              "      <td>0.0582</td>\n",
              "      <td>0.1099</td>\n",
              "      <td>0.1083</td>\n",
              "      <td>0.0974</td>\n",
              "      <td>0.2280</td>\n",
              "      <td>0.2431</td>\n",
              "      <td>0.3771</td>\n",
              "      <td>0.5598</td>\n",
              "      <td>0.6194</td>\n",
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              "      <td>0.0180</td>\n",
              "      <td>0.0244</td>\n",
              "      <td>0.0316</td>\n",
              "      <td>0.0164</td>\n",
              "      <td>0.0095</td>\n",
              "      <td>0.0078</td>\n",
              "      <td>R</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.0100</td>\n",
              "      <td>0.0171</td>\n",
              "      <td>0.0623</td>\n",
              "      <td>0.0205</td>\n",
              "      <td>0.0205</td>\n",
              "      <td>0.0368</td>\n",
              "      <td>0.1098</td>\n",
              "      <td>0.1276</td>\n",
              "      <td>0.0598</td>\n",
              "      <td>0.1264</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0121</td>\n",
              "      <td>0.0036</td>\n",
              "      <td>0.0150</td>\n",
              "      <td>0.0085</td>\n",
              "      <td>0.0073</td>\n",
              "      <td>0.0050</td>\n",
              "      <td>0.0044</td>\n",
              "      <td>0.0040</td>\n",
              "      <td>0.0117</td>\n",
              "      <td>R</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.0762</td>\n",
              "      <td>0.0666</td>\n",
              "      <td>0.0481</td>\n",
              "      <td>0.0394</td>\n",
              "      <td>0.0590</td>\n",
              "      <td>0.0649</td>\n",
              "      <td>0.1209</td>\n",
              "      <td>0.2467</td>\n",
              "      <td>0.3564</td>\n",
              "      <td>0.4459</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0031</td>\n",
              "      <td>0.0054</td>\n",
              "      <td>0.0105</td>\n",
              "      <td>0.0110</td>\n",
              "      <td>0.0015</td>\n",
              "      <td>0.0072</td>\n",
              "      <td>0.0048</td>\n",
              "      <td>0.0107</td>\n",
              "      <td>0.0094</td>\n",
              "      <td>R</td>\n",
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              "      <th>5</th>\n",
              "      <td>0.0286</td>\n",
              "      <td>0.0453</td>\n",
              "      <td>0.0277</td>\n",
              "      <td>0.0174</td>\n",
              "      <td>0.0384</td>\n",
              "      <td>0.0990</td>\n",
              "      <td>0.1201</td>\n",
              "      <td>0.1833</td>\n",
              "      <td>0.2105</td>\n",
              "      <td>0.3039</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0045</td>\n",
              "      <td>0.0014</td>\n",
              "      <td>0.0038</td>\n",
              "      <td>0.0013</td>\n",
              "      <td>0.0089</td>\n",
              "      <td>0.0057</td>\n",
              "      <td>0.0027</td>\n",
              "      <td>0.0051</td>\n",
              "      <td>0.0062</td>\n",
              "      <td>R</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>0.0187</td>\n",
              "      <td>0.0346</td>\n",
              "      <td>0.0168</td>\n",
              "      <td>0.0177</td>\n",
              "      <td>0.0393</td>\n",
              "      <td>0.1630</td>\n",
              "      <td>0.2028</td>\n",
              "      <td>0.1694</td>\n",
              "      <td>0.2328</td>\n",
              "      <td>0.2684</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0116</td>\n",
              "      <td>0.0098</td>\n",
              "      <td>0.0199</td>\n",
              "      <td>0.0033</td>\n",
              "      <td>0.0101</td>\n",
              "      <td>0.0065</td>\n",
              "      <td>0.0115</td>\n",
              "      <td>0.0193</td>\n",
              "      <td>0.0157</td>\n",
              "      <td>M</td>\n",
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              "      <th>204</th>\n",
              "      <td>0.0323</td>\n",
              "      <td>0.0101</td>\n",
              "      <td>0.0298</td>\n",
              "      <td>0.0564</td>\n",
              "      <td>0.0760</td>\n",
              "      <td>0.0958</td>\n",
              "      <td>0.0990</td>\n",
              "      <td>0.1018</td>\n",
              "      <td>0.1030</td>\n",
              "      <td>0.2154</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0061</td>\n",
              "      <td>0.0093</td>\n",
              "      <td>0.0135</td>\n",
              "      <td>0.0063</td>\n",
              "      <td>0.0063</td>\n",
              "      <td>0.0034</td>\n",
              "      <td>0.0032</td>\n",
              "      <td>0.0062</td>\n",
              "      <td>0.0067</td>\n",
              "      <td>M</td>\n",
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              "    <tr>\n",
              "      <th>205</th>\n",
              "      <td>0.0522</td>\n",
              "      <td>0.0437</td>\n",
              "      <td>0.0180</td>\n",
              "      <td>0.0292</td>\n",
              "      <td>0.0351</td>\n",
              "      <td>0.1171</td>\n",
              "      <td>0.1257</td>\n",
              "      <td>0.1178</td>\n",
              "      <td>0.1258</td>\n",
              "      <td>0.2529</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0160</td>\n",
              "      <td>0.0029</td>\n",
              "      <td>0.0051</td>\n",
              "      <td>0.0062</td>\n",
              "      <td>0.0089</td>\n",
              "      <td>0.0140</td>\n",
              "      <td>0.0138</td>\n",
              "      <td>0.0077</td>\n",
              "      <td>0.0031</td>\n",
              "      <td>M</td>\n",
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              "      <th>206</th>\n",
              "      <td>0.0303</td>\n",
              "      <td>0.0353</td>\n",
              "      <td>0.0490</td>\n",
              "      <td>0.0608</td>\n",
              "      <td>0.0167</td>\n",
              "      <td>0.1354</td>\n",
              "      <td>0.1465</td>\n",
              "      <td>0.1123</td>\n",
              "      <td>0.1945</td>\n",
              "      <td>0.2354</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0086</td>\n",
              "      <td>0.0046</td>\n",
              "      <td>0.0126</td>\n",
              "      <td>0.0036</td>\n",
              "      <td>0.0035</td>\n",
              "      <td>0.0034</td>\n",
              "      <td>0.0079</td>\n",
              "      <td>0.0036</td>\n",
              "      <td>0.0048</td>\n",
              "      <td>M</td>\n",
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              "      <th>207</th>\n",
              "      <td>0.0260</td>\n",
              "      <td>0.0363</td>\n",
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              "      <td>0.0214</td>\n",
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              "      <td>0.0655</td>\n",
              "      <td>0.1400</td>\n",
              "      <td>0.1843</td>\n",
              "      <td>0.2354</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0146</td>\n",
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              "      <td>0.0047</td>\n",
              "      <td>0.0039</td>\n",
              "      <td>0.0061</td>\n",
              "      <td>0.0040</td>\n",
              "      <td>0.0036</td>\n",
              "      <td>0.0061</td>\n",
              "      <td>0.0115</td>\n",
              "      <td>M</td>\n",
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              "          const element = document.querySelector('#df-8a77b067-3da3-4cb3-8dd0-a49744e9f5e5');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\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",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Identification of type of dataset using Gandharva's Constant**"
      ],
      "metadata": {
        "id": "nH542Ihd0qO3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "type_of_dataset = None\n",
        "r = df.shape\n",
        "last_ind = r[1]-1\n",
        "total_entries = r[0]-1\n",
        "set_contents = set(df[last_ind])\n",
        "gandharva_constant = len(set_contents)/total_entries\n",
        "if(gandharva_constant>=0.4):\n",
        "  type_of_dataset = 'regression'\n",
        "else:\n",
        "  type_of_dataset = 'classification'\n",
        "type_of_dataset"
      ],
      "metadata": {
        "id": "b2mutJbCvwMg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "5099de5d-70d6-4778-fbc8-8bb557c27d51"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'classification'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Now splitting the dataset**"
      ],
      "metadata": {
        "id": "DRbpFu-F2XhM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split,GridSearchCV,RandomizedSearchCV\n",
        "\n",
        "X = (df.drop(columns = last_ind,axis = 1))\n",
        "Y = np.asarray(df[last_ind])\n",
        "X_train,X_test,Y_train,Y_test = train_test_split(X,Y,test_size = 0.2,random_state = 1)"
      ],
      "metadata": {
        "id": "eFTDyGNjzizn"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Input values for prediction**"
      ],
      "metadata": {
        "id": "gh034Z_nFIaf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "input_data = (0.0139,0.0222,0.0089,0.0108,0.0215,0.0136,0.0659,0.0954,0.0786,0.1015,0.1261,0.0828,0.0493,0.0848,0.1514,0.1396,0.1066,0.1923,0.2991,0.3247,0.3797,0.5658,0.7483,0.8757,0.9048,0.7511,0.6858,0.7043,0.5864,0.3773,0.2206,0.2628,0.2672,0.2907,0.1982,0.2288,0.3186,0.2871,0.2921,0.2806,0.2682,0.2112,0.1513,0.1789,0.1850,0.1717,0.0898,0.0656,0.0445,0.0110,0.0024,0.0062,0.0072,0.0113,0.0012,0.0022,0.0025,0.0059,0.0039,0.0048)"
      ],
      "metadata": {
        "id": "5RA7_6qHFYiR"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# input_data = (4)"
      ],
      "metadata": {
        "id": "nF02c2Qv1QUl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **If dataset is Classification**"
      ],
      "metadata": {
        "id": "UhLBbqHv3_sa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn import svm\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn import tree\n",
        "\n",
        "from sklearn.metrics import accuracy_score,confusion_matrix\n",
        "if(type_of_dataset == 'classification'):\n",
        "  algo_names = ['svm','random_forest','logistic_regression','decision_tree']\n",
        "  algo = [svm.SVC(),RandomForestClassifier(),LogisticRegression(),tree.DecisionTreeClassifier()]\n",
        "  model_params = {\n",
        "   'svm':{\n",
        "   'model':svm.SVC(gamma='auto'),\n",
        "   'params':{\n",
        "       'C':[1,10,20],\n",
        "       'kernel':['linear','poly','rbf']\n",
        "\n",
        "   }\n",
        "  } ,\n",
        "  'random_forest':{\n",
        "       'model':RandomForestClassifier(),\n",
        "       'params':{\n",
        "           'n_estimators':[1,5,10,50,100,150,500,1000]\n",
        "       }\n",
        "   },\n",
        "   'logistic_regression':{\n",
        "       'model':LogisticRegression(solver = 'liblinear',multi_class='auto'),\n",
        "       'params':{\n",
        "          'C':[1,5,10,50,100,150,500,1000]\n",
        "       }\n",
        "   },\n",
        "   'decision_tree':{\n",
        "       'model':tree.DecisionTreeClassifier(),\n",
        "       'params':{\n",
        "           'criterion':['gini','entropy','log_loss'],\n",
        "           'splitter':['best','random']\n",
        "       }\n",
        "   },\n",
        "    }\n",
        "  scores = []\n",
        "  for model_name,mp in model_params.items():\n",
        "    clf = GridSearchCV(mp['model'],mp['params'],cv =5,return_train_score=False)\n",
        "    clf.fit(X_train,Y_train)\n",
        "    scores.append({\n",
        "      'model_name':model_name,\n",
        "      'best_score':clf.best_score_,\n",
        "      'best_parameters':clf.best_params_\n",
        "    }\n",
        "    )\n",
        "  a = []\n",
        "  b = []\n",
        "  final_scores = list(scores)\n",
        "  for i in range(len(final_scores)):\n",
        "    a.append(list(final_scores[i].values()))\n",
        "  for i in range(len(a)):\n",
        "    b.append(a[i][1])\n",
        "  maxi_ind = -1\n",
        "  for i in range(len(b)):\n",
        "    if(b[i] == max(b)):\n",
        "      maxi_ind = i\n",
        "      break\n",
        "  model = algo[maxi_ind]\n",
        "  model.fit(X_train,Y_train)\n",
        "  X_test_prediction = model.predict(X_test)\n",
        "  ml_final_result_accuracy = accuracy_score(X_test_prediction,Y_test)\n",
        "  \n",
        "  input_data_as_np_array = np.asarray(input_data)\n",
        "  input_data_reshaped = input_data_as_np_array.reshape(1,-1)\n",
        "  prediction = model.predict(input_data_reshaped)"
      ],
      "metadata": {
        "id": "lzwYuTyD3mxv"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "prediction[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "eDQwIwA8Axhx",
        "outputId": "4d69e9b1-c526-42d4-89a0-4cb77e865abf"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'R'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Report Printing\n",
        "if(type_of_dataset == 'classification'):\n",
        "  arr = ['Model: ','Accuracy: ', 'Parameters Tuned: ']\n",
        "  print(\"********************* Report ***********************************\")\n",
        "  print()\n",
        "  print(\"Prediction for your values: \",prediction[0])\n",
        "  print(\"Type of dataset: \",type_of_dataset)\n",
        "  for i in range(len(a[maxi_ind])):\n",
        "    print(arr[i],a[maxi_ind][i])\n",
        "  print()\n",
        "  print(\"****************************************************************\")\n"
      ],
      "metadata": {
        "id": "uoCCfyb5DJ-6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "024739c3-8311-4be7-9857-d61d072725d1"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "********************* Report ***********************************\n",
            "\n",
            "Prediction for your values:  R\n",
            "Type of dataset:  classification\n",
            "Model:  random_forest\n",
            "Accuracy:  0.8424242424242424\n",
            "Parameters Tuned:  {'n_estimators': 100}\n",
            "\n",
            "****************************************************************\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **If dataset is Regression**"
      ],
      "metadata": {
        "id": "X0j-c5Xz9yZd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.ensemble import GradientBoostingRegressor\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "\n",
        "if(type_of_dataset == 'regression'):\n",
        "  algo_names = ['Linear Regression','CGBoost Regressor']\n",
        "  algo = [LinearRegression(),GradientBoostingRegressor()]\n",
        "  model_params = {\n",
        "      'linear_regression':{\n",
        "   'model':LinearRegression(),\n",
        "   'params':{\n",
        "      #  'fit_intercept': 1\n",
        "   }\n",
        "  } ,\n",
        "  'random_forest':{\n",
        "       'model':GradientBoostingRegressor(),\n",
        "       'params':{\n",
        "           'n_estimators':[1,5,10,50,100,150,500,1000],\n",
        "           \"learning_rate\": [0.01,0.1,0.5]\n",
        "       }\n",
        "   },\n",
        "  }\n",
        "  scores = []\n",
        "  for model_name,mp in model_params.items():\n",
        "    clf = GridSearchCV(mp['model'],mp['params'],cv =5,return_train_score=False)\n",
        "    clf.fit(X_train,Y_train)\n",
        "    scores.append({\n",
        "      'model_name':model_name,\n",
        "      'best_score':clf.best_score_,\n",
        "      'best_parameters':clf.best_params_\n",
        "    }\n",
        "    )\n",
        "  a = []\n",
        "  b = []\n",
        "  final_scores = list(scores)\n",
        "  for i in range(len(final_scores)):\n",
        "    a.append(list(final_scores[i].values()))\n",
        "  for i in range(len(a)):\n",
        "    b.append(a[i][1])\n",
        "  maxi_ind = -1\n",
        "  for i in range(len(b)):\n",
        "    if(b[i] == max(b)):\n",
        "      maxi_ind = i\n",
        "      break\n",
        "  model = algo[maxi_ind]\n",
        "  model.fit(X_train,Y_train)\n",
        "  X_test_prediction = model.predict(X_test)\n",
        "  # ml_final_result_accuracy = accuracy_score(X_test_prediction,Y_test)\n",
        "  input_data_as_np_array = np.asarray(input_data)\n",
        "  input_data_reshaped = input_data_as_np_array.reshape(1,-1)\n",
        "  prediction = model.predict(input_data_reshaped)"
      ],
      "metadata": {
        "id": "WYap-pxr9kcL"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "prediction"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V_LBJhOp1pRc",
        "outputId": "b64a97bd-6ecb-4492-a1fe-ed6b6f24b86d"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['R'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Report Printing\n",
        "if(type_of_dataset == 'regression'):\n",
        "  arr = ['Model: ','Accuracy: ', 'Parameters Tuned: ']\n",
        "  print(\"********************* Report ***********************************\")\n",
        "  print()\n",
        "  print(\"Prediction for your values: \",prediction[0])\n",
        "  print(\"Type of dataset: \",type_of_dataset)\n",
        "  for i in range(len(a[maxi_ind])):\n",
        "    print(arr[i],a[maxi_ind][i])\n",
        "  print()\n",
        "  print(\"****************************************************************\")\n"
      ],
      "metadata": {
        "id": "1bOkQXWP-x1W"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "lPkoUToRE8lW"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}