This repository contains the projects completed during my Data Science Internship at CodeAlpha. The internship provided practical exposure to data analysis, data visualization, machine learning, and predictive modeling using real-world datasets.
The projects demonstrate the complete data science workflow, from data collection and preprocessing to model development, evaluation, and business insights generation.
| Field | Information |
|---|---|
| Name | Radhika |
| Domain | Data Science |
| Organization | CodeAlpha |
Data Collection
↓
Data Cleaning & Preprocessing
↓
Exploratory Data Analysis
↓
Feature Engineering
↓
Machine Learning Modeling
↓
Model Evaluation
↓
Business Insights & Conclusions
CodeAlpha_DataScience_Internship
│
├── Task1_Iris_Flower_Classification
├── Task2_Unemployment_Analysis
├── Task3_Car_Price_Prediction
└── Task4_Sales_Prediction
Develop a machine learning classification model capable of predicting Iris flower species based on floral measurements.
- Data Exploration
- Data Visualization
- Classification Modeling
- Model Evaluation
- Decision Tree Classifier
- Classification Techniques
- Feature Analysis
- Performance Evaluation
Analyze unemployment trends across different regions of India and identify meaningful patterns through data visualization and statistical analysis.
- Data Cleaning
- Exploratory Data Analysis
- Correlation Analysis
- Trend Analysis
- Data Visualization
- Data Analysis
- Statistical Interpretation
- Trend Identification
- Data Storytelling
Develop a regression model to estimate used car selling prices based on vehicle-related attributes.
- Data Preprocessing
- Feature Engineering
- Categorical Encoding
- Regression Modeling
- Performance Evaluation
- Linear Regression
- Regression Analysis
- Feature Engineering
- Predictive Modeling
Predict product sales based on advertising expenditure across multiple marketing channels.
- Exploratory Data Analysis
- Correlation Analysis
- Regression Modeling
- Prediction and Evaluation
- Linear Regression
- Sales Forecasting
- Business Analytics
- Predictive Modeling
- Python
- Pandas
- NumPy
- Matplotlib
- Seaborn
- Scikit-learn
- Google Colab
- GitHub
- Data Cleaning and Preprocessing
- Exploratory Data Analysis
- Data Visualization
- Machine Learning Classification
- Machine Learning Regression
- Feature Engineering
- Model Evaluation
- Predictive Analytics
- Business Insight Generation
During this internship, I gained practical experience in:
- Building end-to-end machine learning projects
- Applying classification and regression algorithms
- Performing exploratory data analysis
- Creating meaningful visualizations
- Evaluating model performance
- Extracting actionable business insights from data
The CodeAlpha Data Science Internship provided valuable hands-on experience in real-world data science applications. Through these projects, I strengthened my understanding of data preprocessing, exploratory analysis, machine learning modeling, and business-oriented problem solving.
This repository reflects the practical implementation of data science methodologies and demonstrates my ability to transform raw data into meaningful insights and predictive solutions.