https://github.com/mamtapanda088/uber-data-analysis

Uber data analysis involves studying trip trends, peak hours, popular locations, fare patterns, and visualizing insights using Python.
https://github.com/mamtapanda088/uber-data-analysis

jupyter-notebook matplotlib numpy pandas python3 seaborn

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Uber data analysis involves studying trip trends, peak hours, popular locations, fare patterns, and visualizing insights using Python.

• Host: GitHub
• URL: https://github.com/mamtapanda088/uber-data-analysis
• Owner: mamtapanda088
• Created: 2025-01-30T02:47:34.000Z (12 months ago)
• Default Branch: main
• Last Pushed: 2025-01-30T02:53:00.000Z (12 months ago)
• Last Synced: 2025-01-30T03:25:24.874Z (12 months ago)
• Topics: jupyter-notebook, matplotlib, numpy, pandas, python3, seaborn
• Language: Jupyter Notebook
• Homepage:
• Size: 0 Bytes
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
Uber Data Analysis: Step-by-Step Guide

Objective

The goal of this analysis is to explore Uber trip data to derive meaningful insights, identify trends, and make data-driven decisions.

Steps and Procedure

1. Data Collection

Obtain the dataset: Uber provides open-source trip data, or you can use a publicly available dataset from sources like Kaggle.

Example of data fields:

Date/Time: Date and time of the trip.

Lat, Lon: Latitude and longitude of pickup.

Base: Uber base code.

2. Data Preprocessing

Load the Dataset: Use Python libraries like pandas to load the data into a DataFrame.

Handle Missing Values:

Check for null or missing values using df.isnull().sum().

Drop or fill missing values depending on their impact on analysis.

Convert Datatypes:

Convert date columns into a datetime object for easier manipulation (pd.to_datetime).

Remove Duplicates:

Ensure there are no duplicate rows to maintain data integrity.

3. Exploratory Data Analysis (EDA)

Basic Statistics:

Use df.describe() to get insights into numerical features.

Feature Engineering:

Extract new columns from Date/Time:

Hour, Day, Weekday, Month.

Example:

python

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Edit

df['hour'] = df['Date/Time'].dt.hour

df['day'] = df['Date/Time'].dt.day

df['weekday'] = df['Date/Time'].dt.weekday

df['month'] = df['Date/Time'].dt.month

Data Visualization:

Plot trip frequency across time (hours, days, weekdays) using matplotlib or seaborn.

Analyze pickup locations using geographic visualizations (folium or plotly).

4. Data Visualization

Time-based Trends:

Identify peak hours and days using bar plots or line charts.

Example: Which hours have the most rides?

Geospatial Analysis:

Visualize pickup density using heatmaps.

Libraries: folium, plotly.express.

Base Code Analysis:

Check which Uber base (e.g., B02512) had the highest number of trips.

5. Insights and Findings

Summarize key observations from the analysis:

Peak usage hours (e.g., morning rush hours).

Popular pickup locations.

Seasonal trends (e.g., increase in rides during holidays or weekends).

Example Insights:

Most rides occur during weekends and evenings.

High demand is concentrated in central city areas.

6. Conclusion and Recommendations

Use insights to make recommendations:

Suggest increasing the number of drivers during peak hours.

Highlight areas for promotional offers based on high demand.

7. Tools and Libraries Used

Programming Language: Python

Libraries:

pandas: Data manipulation and analysis.

matplotlib, seaborn: Data visualization.

folium, plotly: Geospatial analysis.

numpy: Numerical computations.

8. Future Scope

Incorporate weather data to understand its effect on ride demand.

Perform predictive modeling to forecast ride demand.

Analyze driver performance and customer satisfaction ratings.