Merge pull request #723 from Varunshiyam/Fixes-#717-Taxi

Fixes #717 Taxi Fare prediction

Author: UppuluriKalyani

Prediction Models/Taxi-Fare_Prediction/README.md

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+# Taxi Fare Prediction with Keras Deep Learning
+
+## Project Overview
+This project focuses on building a predictive model to estimate taxi fares using deep learning techniques with the Keras library. By training a neural network on historical trip data, which includes factors such as pickup and drop-off locations, trip distance, and duration, the model aims to provide accurate fare predictions. This solution can be useful for real-time fare estimation in ride-hailing services, enhancing pricing transparency.
+
+## Features
+- **Deep Learning Model**: Built with Keras for regression tasks to predict taxi fares.
+- **Data Preprocessing**: Includes data cleaning and feature engineering to improve model performance.
+- **Scalability**: Model architecture can be adapted to other regions or similar fare prediction problems.
+
+## Technical Requirements
+- **Python**: Version 3.6 or above
+- **Libraries**: 
+  - `keras`
+  - `tensorflow`
+  - `pandas`
+  - `numpy`
+  - `scikit-learn`
+- **Data**: Historical trip data, including fields like pickup/drop-off locations, distance, and trip duration.
+
+## Installation
+1. Clone this repository:
+    ```bash
+    git clone https://github.com/your-username/taxi-fare-prediction.git
+    cd taxi-fare-prediction
+    ```
+
+2. Install the required dependencies:
+    ```bash
+    pip install -r requirements.txt
+    ```
+
+
+
+## Folder Structure
+- `train_model.py` : Contains code to train the neural network model.
+- `predict.py` : Allows for fare predictions based on input data.
+- `data/` : Directory for input data files.
+- `models/` : Directory where trained models are saved.
+
+## Problem Statement
+In the current taxi and ride-hailing services, fare estimation often lacks accuracy due to variable factors like trip distance, time of day, and traffic conditions. This unpredictability can lead to customer dissatisfaction and distrust. This project aims to develop a reliable predictive model that learns from historical data, offering more accurate fare estimations and enhancing user experience in ride-hailing platforms.
+
+By using a deep learning model, this solution leverages Keras to capture complex patterns in fare data, ultimately helping to provide transparent, accurate, and scalable fare predictions.