Trips USA Analysis

Table of Contents

Project Overview
Dataset Description
Features
Prerequisites
Installation
Usage
Notebook Structure
Results

Project Overview

The Trips USA Analysis project aims to explore and visualize trip data across the United States. By leveraging powerful Python libraries such as pandas, Dask, and matplotlib, this analysis provides insights into trip patterns, durations, and geographic distributions.

Dataset Description

The dataset (trips.csv) contains records of trips made across various US cities, including:

Trip ID: Unique identifier for each trip
Start & End Timestamp: Date and time information
Origin & Destination: City or state coordinates
Trip Duration: Duration in minutes
User Attributes: Optional demographic information

Features

Efficient data loading and preprocessing with Dask for large datasets
Exploratory data analysis (EDA) to uncover key trends
Interactive and static visualizations using matplotlib and seaborn
Parallel processing with joblib for performance optimization

Prerequisites

Python 3.7 or higher
Git

Installation

Clone the repository:

git clone <repository-url>
cd <repository-directory>

Create & activate a virtual environment (optional but recommended):
```
python3 -m venv env
source env/bin/activate
```
Install dependencies:
```
pip install -r requirements.txt
```

Alternatively, using Conda:

conda create --name trips_usa python=3.9
conda activate trips_usa
pip install -r requirements.txt

Usage

Ensure your data is in data/trips.csv.
Launch Jupyter Notebook:
```
jupyter notebook
```
Open trips_usa.ipynb and run all cells.
Explore the visualizations and notebooks in the notebooks/ folder for additional analyses.

Notebook Structure

01_Data_Loading.ipynb: Setup environment and load raw data.
02_Preprocessing.ipynb: Clean and transform data.
03_Exploratory_Analysis.ipynb: Generate summary statistics and plots.
04_Visualizations.ipynb: Create advanced charts and maps.

Results

Distribution of trip durations
Peak travel times by hour and day of week
Geographic heatmaps of trip origins and destinations
Correlation analysis between variables

5004CMD2425JANMAY/dass25_5004_CMD

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