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🚖 Uber Fare Prediction: Combining Machine Learning with Streamlit for Real-Time Fare Estimates 🌟

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Machine Learning
Sethumadhavanmis
@Sethumadhavanmis
Sethumadhavanmis
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I’m excited to share my latest project: a machine learning model for predicting Uber ride fares, paired with a sleek Streamlit web application for real-time fare estimates!

🛠️ Project Overview

This project leverages machine learning techniques to predict Uber fares based on various ride details. The goal is to provide accurate fare estimates, which can be useful for both customers and businesses. The Streamlit app allows users to input ride details and get fare predictions effortlessly.

🔍 Skills Acquired

  • Data Cleaning and Preprocessing: Transforming raw data into a usable format.

  • Feature Engineering: Creating meaningful features for model training.

  • Exploratory Data Analysis (EDA): Gaining insights through data visualization.

  • Regression Modeling: Building a model to predict fare amounts.

  • Hyperparameter Tuning: Optimizing model performance.

  • Model Evaluation: Assessing model accuracy and effectiveness.

  • Geospatial and Time Series Analysis: Understanding spatial and temporal patterns.

  • Web Application Development: Creating interactive applications using Streamlit.

  • Cloud Deployment: Hosting the application on AWS for global access.

❓ Problem Statement

Develop a predictive model for Uber ride fares using historical data and create a user-friendly Streamlit application to provide real-time fare estimates based on input ride details.

💼 Business Use Cases

  • Fare Estimation: Provide users with accurate fare estimates before booking.

  • Dynamic Pricing: Adjust fare estimates based on demand and time of day.

  • Resource Allocation: Optimize fleet management by predicting high-demand areas.

  • User Engagement: Enhance user experience with reliable fare predictions.

🛠️ Approach

  1. Upload Dataset: Start by uploading ride data to an S3 bucket.

  2. Data Preprocessing: Clean and preprocess the data, including handling missing values and feature extraction.

  3. Model Training: Train a regression model and save it for future predictions.

  4. Streamlit Application: Develop an intuitive web interface for users to get fare estimates.

  5. Deployment: Deploy the application on AWS for seamless access.

🏆 Results

  • Trained Model: Accurately predicts Uber fares.

  • Streamlit App: An interactive tool for users to input ride details and receive fare estimates.

  • Evaluation Metrics: Detailed performance metrics showcasing the model's reliability.

📊 Dataset Details

  • Source: Uber ride data in CSV format.

  • Variables: Includes fare_amount, pickup/dropoff locations, datetime, and passenger count.

🚀 Getting Started

  • Clone the Repository: git clone https://github.com/SETHU0010/uber-fare-prediction.git

  • Install Dependencies: pip install -r requirements.txt

  • Run the App: streamlit run app.py

The web application is deployed and accessible here for live demonstrations. Feel free to check it out and let me know your thoughts!

📋 Project Guidelines

  • Coding Standards: Follow PEP 8 guidelines.

  • Version Control: Use Git for maintaining code.

  • Documentation: Ensure clear instructions and code comments.

  • Best Practices: Validate models, ensure reproducibility, and manage data privacy.

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