Delete Untitled2.ipynb

Untitled2.ipynb

@@ -1,158 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "2a0f61a3",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Accuracy: 0.8417508417508418\n",
-      " * Serving Flask app \"__main__\" (lazy loading)\n",
-      " * Environment: production\n",
-      "\u001b[31m   WARNING: This is a development server. Do not use it in a production deployment.\u001b[0m\n",
-      "\u001b[2m   Use a production WSGI server instead.\u001b[0m\n",
-      " * Debug mode: on\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " * Restarting with watchdog (windowsapi)\n"
-     ]
-    },
-    {
-     "ename": "SystemExit",
-     "evalue": "1",
-     "output_type": "error",
-     "traceback": [
-      "An exception has occurred, use %tb to see the full traceback.\n",
-      "\u001b[1;31mSystemExit\u001b[0m\u001b[1;31m:\u001b[0m 1\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\91958\\anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3377: UserWarning: To exit: use 'exit', 'quit', or Ctrl-D.\n",
-      "  warn(\"To exit: use 'exit', 'quit', or Ctrl-D.\", stacklevel=1)\n"
-     ]
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "from flask import Flask, request, jsonify\n",
-    "\n",
-    "from sklearn.compose import ColumnTransformer\n",
-    "from sklearn.ensemble import RandomForestClassifier\n",
-    "from sklearn.impute import SimpleImputer\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.pipeline import Pipeline\n",
-    "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
-    "\n",
-    "# Load the CSV data\n",
-    "data = pd.read_csv('dataset.csv')\n",
-    "\n",
-    "# Split the data into features and labels\n",
-    "X = data.drop('PlacedOrNot', axis=1)\n",
-    "y = data['PlacedOrNot']\n",
-    "\n",
-    "# Encode categorical features\n",
-    "categorical_features = ['HistoryOfBacklogs']\n",
-    "for feature in categorical_features:\n",
-    "    encoder = LabelEncoder()\n",
-    "    X[feature] = encoder.fit_transform(X[feature])\n",
-    "\n",
-    "# Split the data into training and testing sets\n",
-    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
-    "\n",
-    "# Create the pipeline\n",
-    "numerical_features = ['Internships', 'CGPA']\n",
-    "numerical_transformer = StandardScaler()\n",
-    "categorical_features = [ 'HistoryOfBacklogs']\n",
-    "categorical_transformer = SimpleImputer(strategy='most_frequent')\n",
-    "preprocessor = ColumnTransformer(\n",
-    "    transformers=[\n",
-    "        ('num', numerical_transformer, numerical_features),\n",
-    "        ('cat', categorical_transformer, categorical_features)\n",
-    "    ])\n",
-    "\n",
-    "pipeline = Pipeline([\n",
-    "    ('preprocessor', preprocessor),\n",
-    "    ('classifier', RandomForestClassifier(random_state=42))\n",
-    "])\n",
-    "\n",
-    "# Train the model\n",
-    "pipeline.fit(X_train, y_train)\n",
-    "\n",
-    "# Evaluate the model\n",
-    "accuracy = pipeline.score(X_test, y_test)\n",
-    "print('Accuracy:', accuracy)\n",
-    "\n",
-    "# Create Flask app\n",
-    "app = Flask(__name__)\n",
-    "\n",
-    "# Define API route for making predictions\n",
-    "@app.route('/predict', methods=['POST'])\n",
-    "def predict():\n",
-    "    # Get input data from request\n",
-    "    data = request.get_json()\n",
-    "\n",
-    "    # Convert input data to dataframe\n",
-    "    input_data = pd.DataFrame(data, index=[0])\n",
-    "\n",
-    "    # Make predictions using the trained pipeline\n",
-    "    predictions = pipeline.predict(input_data)\n",
-    "\n",
-    "    # Prepare response\n",
-    "    response = {'prediction': predictions[0]}\n",
-    "    return jsonify(response)\n",
-    "\n",
-    "# Run the Flask app\n",
-    "if __name__ == '__main__':\n",
-    "    app.run(debug=True)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8e941b77",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4a2788a3",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "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"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}