A collection of CSV datasets used for practicing data analysis with Pandas and building Machine Learning models.
These datasets are part of my learning journey in Data Science and Machine Learning. They are useful for exploring data, cleaning datasets, creating visualizations and training predictive models.
Customer segmentation dataset commonly used for clustering and unsupervised learning algorithms.
Typical use cases:
- K-Means Clustering
- Customer Segmentation
- Exploratory Data Analysis (EDA)
Simple dataset used for regression models.
Typical use cases:
- Linear Regression
- Model Evaluation
- Predicting salaries
Classification dataset frequently used to predict user behavior.
Typical use cases:
- Logistic Regression
- Decision Trees
- K-Nearest Neighbors (KNN)
- Support Vector Machines (SVM)
- CSV
- Python
- Pandas
- NumPy
- Scikit-learn
- Jupyter Notebook
- Reading CSV files with Pandas
- Data preprocessing
- Feature engineering
- Exploratory Data Analysis
- Supervised Learning
- Unsupervised Learning
- Model training and evaluation
import pandas as pd
df = pd.read_csv("Salary_Data.csv")
print(df.head())These datasets are intended for educational purposes and personal practice while learning Machine Learning with Python, and its libraries.