Data Analytics with Pandas and Visualization in Google Colab

Ссылки

File Untitled6.ipynb

Overview

This project is a Google Colab notebook that demonstrates data analytics and visualization using the California housing dataset (california_housing_train.csv). The notebook utilizes pandas, matplotlib, and seaborn to explore dependencies, trends, and correlations in the dataset.

Features

• Data Loading & Preprocessing: Reads the dataset into a Pandas DataFrame and performs basic cleaning.
• Descriptive Statistics: Computes statistical summaries such as mean, median, standard deviation, and correlations.
• Data Visualization: Uses various visualization techniques to understand data relationships, trends, and distributions.
• Exploratory Data Analysis (EDA): Includes scatter plots, boxplots, heatmaps, and histogram analysis.
• Regression & Correlation Analysis: Investigates dependencies between features using scatter plots and regression lines.
• Geospatial Analysis: Visualizes housing data based on longitude and latitude.

Requirements

Ensure the following libraries are installed in your Colab environment:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

Usage

1. Open Google Colab and upload california_housing_train.csv (if not present in sample_data/).
2. Run the notebook cells to analyze the dataset.
3. Customize the plots and analysis according to your requirements.

Key Visualizations

• Histogram: Distribution of numerical features.
• Heatmap: Correlation between different attributes.
• Scatter Plot: Relationship between median_income and median_house_value.
• Box Plot: Identifies outliers and spread in numerical features.
• Hexbin Plot: Density visualization for large datasets.

Example: Correlation Matrix

plt.figure(figsize=(10, 6))
sns.heatmap(df.corr(), annot=True, cmap="coolwarm", fmt=".2f", linewidths=1)
plt.title("Correlation Matrix")
plt.show()

Conclusion

This Colab notebook provides a foundation for data analysis and visualization in Python using pandas, matplotlib, and seaborn. It helps identify trends, patterns, and relationships in the dataset, which can be useful for predictive modeling and further machine learning applications.

License

This project is open-source and available under the MIT License.