prediction

abhisheks008 · Aug 2, 2024 · 864f888 · 864f888
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diff --git a/.DS_Store b/.DS_Store
diff --git a/Data Analytics Salary Prediction/Images/avdslr_peryear_loc b/Data Analytics Salary Prediction/Images/avdslr_peryear_loc
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diff --git a/Data Analytics Salary Prediction/Images/top_6_high_pay_company b/Data Analytics Salary Prediction/Images/top_6_high_pay_company
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diff --git a/Data Analytics Salary Prediction/Images/top_6_less_pay_company b/Data Analytics Salary Prediction/Images/top_6_less_pay_company
diff --git a/DataAnalyticsSalaryPrediction/.DS_Store b/DataAnalyticsSalaryPrediction/.DS_Store
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+.venv
diff --git a/...ytics Salary Prediction/Dataset/README.md → ...alyticsSalaryPrediction/Dataset/README.md b/...ytics Salary Prediction/Dataset/README.md → ...alyticsSalaryPrediction/Dataset/README.md
diff --git a/...ary Prediction/Dataset/Salary Dataset.csv → ...laryPrediction/Dataset/Salary Dataset.csv b/...ary Prediction/Dataset/Salary Dataset.csv → ...laryPrediction/Dataset/Salary Dataset.csv
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diff --git a/...el/Data Analytics Salary Prediction.ipynb → ...ts/Data Analytics Salary Prediction.ipynb b/...el/Data Analytics Salary Prediction.ipynb → ...ts/Data Analytics Salary Prediction.ipynb
diff --git a/...alytics Salary Prediction/Model/README.md → ...AnalyticsSalaryPrediction/Model/README.md b/...alytics Salary Prediction/Model/README.md → ...AnalyticsSalaryPrediction/Model/README.md
diff --git a/DataAnalyticsSalaryPrediction/Model/encoder.pkl b/DataAnalyticsSalaryPrediction/Model/encoder.pkl
diff --git a/DataAnalyticsSalaryPrediction/Model/model.ipynb b/DataAnalyticsSalaryPrediction/Model/model.ipynb
diff --git a/DataAnalyticsSalaryPrediction/Model/model1 b/DataAnalyticsSalaryPrediction/Model/model1
diff --git a/DataAnalyticsSalaryPrediction/Model/requirements.txt b/DataAnalyticsSalaryPrediction/Model/requirements.txt
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+pandas
+numpy
+scikit-learn
+tensorflow
+matplotlib
+keras
diff --git a/DataAnalyticsSalaryPrediction/Model/scaler.pkl b/DataAnalyticsSalaryPrediction/Model/scaler.pkl
diff --git a/DataAnalyticsSalaryPrediction/readme.md b/DataAnalyticsSalaryPrediction/readme.md
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+## Data Analysis Salary Prediction - Web Interface
+
+### Goal 🎯
+
+The main goal of this project is to provide an easy-to-use web interface for predicting Salary of various job posts accross India based on user input parameters. This tool aims to make salary predition accessible to non-technical users by integrating a machine learning model with a user-friendly Flask web application.
+
+### Model(s) used for the Web App 🧮
+
+The backend part of the web app uses a pre-trained machine learning model (`../Model`) serialized with `pickle`. The model was trained on a dataset of body measurements and is designed to predict body fat percentage accurately.
+
+### Video Demonstration 🎥
+
+
+
+### Signature ✒️
+
+Developed by [Akshaykumar](https://github.com/MRMORNINGSTAR2233)
+
+- [GitHub](https://github.com/MRMORNINGSTAR2233)
+- [LinkedIn](https://www.linkedin.com/in/akshay-kumar-hegde/)  
diff --git a/...lytics Salary Prediction/requirements.txt → ...nalyticsSalaryPrediction/requirements.txt b/...lytics Salary Prediction/requirements.txt → ...nalyticsSalaryPrediction/requirements.txt
diff --git a/DataAnalyticsSalaryPrediction/web/app.py b/DataAnalyticsSalaryPrediction/web/app.py
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+import os
+import pickle
+import numpy as np
+from flask import Flask, request, jsonify, render_template
+from sklearn.preprocessing import OneHotEncoder, StandardScaler
+import pandas as pd
+import tensorflow as tf
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense, Dropout, Input
+
+
+app = Flask(__name__)
+
+def create_model(optimizer='adam'):
+    model = Sequential()
+    model.add(Input(shape=(X_resampled.shape[1],)))  # Assuming X_resampled.shape[1] is known
+    model.add(Dense(128, activation='relu', kernel_regularizer=tf.keras.regularizers.l2(0.01)))
+    model.add(Dropout(0.5))
+    model.add(Dense(64, activation='relu', kernel_regularizer=tf.keras.regularizers.l2(0.01)))
+    model.add(Dropout(0.5))
+    model.add(Dense(32, activation='relu', kernel_regularizer=tf.keras.regularizers.l2(0.01)))
+    model.add(Dense(5, activation='softmax'))
+    model.compile(optimizer=optimizer, loss='sparse_categorical_crossentropy', metrics=['accuracy'])
+    return model
+
+# Load the trained model
+with open('/Users/akshay/Desktop/ML-Crate/DataAnalyticsSalaryPrediction/Model/model1', 'rb') as file:
+    model = pickle.load(file)
+
+# Load the encoder and scaler
+with open('/Users/akshay/Desktop/ML-Crate/DataAnalyticsSalaryPrediction/Model/encoder.pkl', 'rb') as file:
+    encoder = pickle.load(file)
+
+with open('/Users/akshay/Desktop/ML-Crate/DataAnalyticsSalaryPrediction/Model/scaler.pkl', 'rb') as file:
+    scaler = pickle.load(file)
+
+# Preprocessing function
+def preprocess_input(data):
+    df = pd.DataFrame(data, index=[0])
+    df_encoded = encoder.transform(df)
+    df_scaled = scaler.transform(df_encoded)
+    return df_scaled
+
+@app.route('/')
+def home():
+    return render_template('index.html')
+
+@app.route('/predict', methods=['POST'])
+def predict():
+    data = request.get_json()
+    input_data = preprocess_input(data)
+    prediction = model.predict(input_data)
+    return jsonify({'prediction': int(prediction[0])})
+
+if __name__ == '__main__':
+    app.run(debug=True)
diff --git a/DataAnalyticsSalaryPrediction/web/templates/index.html b/DataAnalyticsSalaryPrediction/web/templates/index.html
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+<!DOCTYPE html>
+<html lang="en">
+
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Salary Prediction</title>
+    <style>
+        body {
+            font-family: Arial, sans-serif;
+            background-color: #f4f4f4;
+            margin: 0;
+            padding: 0;
+        }
+
+        .container {
+            width: 50%;
+            margin: 50px auto;
+            background: #fff;
+            padding: 20px;
+            box-shadow: 0 0 10px rgba(0, 0, 0, 0.1);
+        }
+
+        h1 {
+            text-align: center;
+        }
+
+        form {
+            display: flex;
+            flex-direction: column;
+        }
+
+        label {
+            margin: 10px 0 5px;
+        }
+
+        input {
+            padding: 10px;
+            font-size: 16px;
+        }
+
+        button {
+            padding: 10px;
+            background: #28a745;
+            color: #fff;
+            border: none;
+            margin-top: 20px;
+            cursor: pointer;
+        }
+
+        button:hover {
+            background: #218838;
+        }
+
+        #result {
+            margin-top: 20px;
+            font-size: 18px;
+            text-align: center;
+        }
+    </style>
+</head>
+
+<body>
+    <div class="container">
+        <h1>Salary Prediction</h1>
+        <form id="prediction-form">
+            <label for="company_name">Company Name:</label>
+            <input type="text" id="company_name" name="Company Name" required>
+
+            <label for="job_title">Job Title:</label>
+            <input type="text" id="job_title" name="Job Title" required>
+
+            <label for="location">Location:</label>
+            <input type="text" id="location" name="Location" required>
+
+            <button type="submit">Predict Salary</button>
+        </form>
+        <div id="result"></div>
+    </div>
+    <script>
+        document.getElementById('prediction-form').addEventListener('submit', function (e) {
+            e.preventDefault();
+
+            const companyName = document.getElementById('company_name').value;
+            const jobTitle = document.getElementById('job_title').value;
+            const location = document.getElementById('location').value;
+
+          const data = {
+                "Company Name": companyName,
+                "Job Title": jobTitle,
+                "Location": location
+            };
+
+            fetch('/predict', {
+                method: 'POST',
+                headers: {
+                    'Content-Type': 'application/json'
+                },
+                body: JSON.stringify(data)
+            })
+                .then(response => response.json())
+                .then(data => {
+                    let salaryRange = '';
+                    switch (data.prediction) {
+                        case 0:
+                            salaryRange = 'greater than 50k';
+                            break;
+                        case 1:
+                            salaryRange = 'greater than 1 lakh';
+                            break;
+                        case 2:
+                            salaryRange = 'greater than 10 lakh';
+                            break;
+                        case 3:
+                            salaryRange = 'greater than 15 lakh';
+                            break;
+                        case 4:
+                            salaryRange = 'greater than 20 lakh';
+                            break;
+                        default:
+                            salaryRange = 'unknown';
+                    }
+                    document.getElementById('result').innerText = `Predicted Salary Range: ${salaryRange}`;
+                })
+                .catch(error => {
+                    console.error('Error:', error);
+                });
+
+                });
+    </script>
+</body>
+
+</html>