Bike Rental Data Analysis and Visualization Project 🚲
Zain Manna
Data Scientist
Data Visualizer
Data Analyst
pandas
Python
seaborn
Project Objective:
The objective of this project was to analyze bike rental data and gain insights into factors affecting bike rental counts. By conducting exploratory data analysis (EDA) and statistical analysis, the goal was to understand the relationships between various variables such as weather conditions, time of day, and rental counts.
Tools Used:
- Programming Language: Python
- Libraries: Pandas, NumPy, Matplotlib, Seaborn
- Environment: Google Colab (Jupyter Notebook)
Project Steps:
- Data Collection and Preprocessing:
- Imported bike rental dataset containing information about rental counts, weather conditions, and time.
- Preprocessed the data by handling missing values, converting data types, and ensuring data integrity.
- Exploratory Data Analysis (EDA):
- Univariate Analysis:
- Examined the distribution of individual variables such as rental counts, temperature, humidity, and wind speed.
- Calculated summary statistics including mean, median, and mode for numerical variables.
- Visualized distributions using histograms and box plots.
- Bivariate Analysis:
- Explored relationships between pairs of variables to understand correlations and dependencies.
- Visualized relationships using scatter plots, line plots, and box plots.
- Analyzed how rental counts vary with weather conditions, time of day, and other factors.
- Temporal Analysis:
- Analyzed temporal trends by resampling data on a monthly basis and visualizing rental counts over time.
- Investigated patterns, seasonality, and trends in bike rental activity.
- Statistical Analysis:
- Conducted correlation analysis to examine relationships between numerical variables.
- Formulated hypotheses and performed hypothesis testing to validate assumptions and identify significant differences.
- Univariate Analysis:
- Statistical Analysis:
- Conducted correlation analysis to examine relationships between numerical variables.
