abhisheks008 · MRMORNINGSTAR2233 · Aug 2, 2024 · Aug 3, 2024 · Aug 3, 2024 · Aug 3, 2024
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
diff --git a/Data Analytics Salary Prediction/Images/avgslr_each_job.png b/Data Analytics Salary Prediction/Images/avgslr_each_job.png
diff --git a/Data Analytics Salary Prediction/Images/avgslr_location.png b/Data Analytics Salary Prediction/Images/avgslr_location.png
diff --git a/Data Analytics Salary Prediction/Images/avgslr_top_10_company.png b/Data Analytics Salary Prediction/Images/avgslr_top_10_company.png
diff --git a/Data Analytics Salary Prediction/Images/top_6_high_pay_company b/Data Analytics Salary Prediction/Images/top_6_high_pay_company
diff --git a/Data Analytics Salary Prediction/Images/top_6_jobs.png b/Data Analytics Salary Prediction/Images/top_6_jobs.png
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/Salary Prediction of Data Analytics with Web App/.DS_Store b/Salary Prediction of Data Analytics with Web App/.DS_Store
diff --git a/Salary Prediction of Data Analytics with Web App/.gitignore b/Salary Prediction of Data Analytics with Web App/.gitignore
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+.venv
+.DS_STORE
diff --git a/...ytics Salary Prediction/Dataset/README.md → ... Analytics with Web App/Dataset/README.md b/...ytics Salary Prediction/Dataset/README.md → ... Analytics with Web App/Dataset/README.md
diff --git a/...ary Prediction/Dataset/Salary Dataset.csv → ...s with Web App/Dataset/Salary Dataset.csv b/...ary Prediction/Dataset/Salary Dataset.csv → ...s with Web App/Dataset/Salary Dataset.csv
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 → ...ta Analytics with Web App/Model/README.md b/...alytics Salary Prediction/Model/README.md → ...ta Analytics with Web App/Model/README.md
diff --git a/Salary Prediction of Data Analytics with Web App/Model/encoder.pkl b/Salary Prediction of Data Analytics with Web App/Model/encoder.pkl
diff --git a/Salary Prediction of Data Analytics with Web App/Model/model.ipynb b/Salary Prediction of Data Analytics with Web App/Model/model.ipynb
diff --git a/Salary Prediction of Data Analytics with Web App/Model/model1 b/Salary Prediction of Data Analytics with Web App/Model/model1
diff --git a/Salary Prediction of Data Analytics with Web App/Model/requirements.txt b/Salary Prediction of Data Analytics with Web App/Model/requirements.txt
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+pandas
+numpy
+scikit-learn
+tensorflow
+matplotlib
+keras
diff --git a/Salary Prediction of Data Analytics with Web App/Model/scaler.pkl b/Salary Prediction of Data Analytics with Web App/Model/scaler.pkl
diff --git a/Salary Prediction of Data Analytics with Web App/Web App/2024-08-03 00-49-28.mkv b/Salary Prediction of Data Analytics with Web App/Web App/2024-08-03 00-49-28.mkv
diff --git a/...of Data Analytics with Web App/Web App/Screenshot 2024-08-03 at 12.46.57 AM.png b/...of Data Analytics with Web App/Web App/Screenshot 2024-08-03 at 12.46.57 AM.png
diff --git a/...of Data Analytics with Web App/Web App/Screenshot 2024-08-03 at 12.47.27 AM.png b/...of Data Analytics with Web App/Web App/Screenshot 2024-08-03 at 12.47.27 AM.png
diff --git a/Salary Prediction of Data Analytics with Web App/Web App/app.py b/Salary Prediction of Data Analytics with Web App/Web App/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/Salary Prediction of Data Analytics with Web App/Web App/templates/index.html b/Salary Prediction of Data Analytics with Web App/Web App/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>
diff --git a/Salary Prediction of Data Analytics with Web App/readme.md b/Salary Prediction of Data Analytics with Web App/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 → ...a Analytics with Web App/requirements.txt b/...lytics Salary Prediction/requirements.txt → ...a Analytics with Web App/requirements.txt