EDA-on-NYC-Taxi-Data

• Conducting an Exploratory Data Analysis (EDA) on New York City taxi data and visualizing it through countplots, distribution plots (displot), and histograms using Python and it's libraries.

To view the project (Google Collab) -> https://colab.research.google.com/drive/19kFrs_tu3P3W4sWwYIQZpRkkkB5Vd7t2

About the project

Exploratory Data Analysis (EDA) is a critical step in data analysis, helping us understand the structure, patterns, and characteristics of a dataset. In this example, we'll perform EDA on New York City taxi data using Python and various libraries. We'll visualize the data using countplots, distribution plots (displot), and histograms to gain insights.In which we performed Descriptive and Diagnostic Analysis. We have used the NYC taxi dataset from Kaggle for this project -> https://www.kaggle.com/competitions/nyc-taxi-trip-duration

Libraries Used:

• Numpy
• Pandas
• Matplotlib
• Seaborn

VISUALIZATIONS

Plot 1. Distribution of Passenger Count

Plot 2. Distribution of each day in a week

Plot 3. Trip Duration Distribution

PLot 4. Distribution of pickup timezone

Plot 5. Distribution of active hours - pickup and drop off

Plot 6: Distribution of pickup and dropoff months

Plot 7. Distribution of total pickup hour