CodeAlpha Tasks

This repository contains three data science projects focusing on prediction and analysis tasks. Each project includes datasets, Python code, and a detailed report.

---

1. CodeAlpha_Iris_Classification

Objective:
Classify Iris flowers into three species (Setosa, Versicolor, Virginica) based on petal and sepal measurements.

Dataset:

• 150 samples with features: Sepal Length, Sepal Width, Petal Length, Petal Width.
• Target column: Species.

Workflow:

1. Data Loading & Exploration: Loaded and inspected dataset with pandas.
2. Visualization: Created pairplots using seaborn; observed clear clusters for each species.
3. Model Building: Used LogisticRegression with an 80/20 train-test split.
4. Evaluation: Achieved 100% accuracy on test data.
5. User Interaction: Added prompts to predict species for new measurements.

Insights:

• Petal and sepal measurements are sufficient for accurate classification.
• The model can automate species identification in botanical applications.

Files:

• CodeAlpha_Iris_Classification/iris_project.py — Python code
• CodeAlpha_Iris_Classification/Iris.csv — Dataset
• CodeAlpha_Iris_Classification/IRIS FLOWER CLASSIFICATION (REPORT).pdf — Report

---

2. CodeAlpha_Car_Price_Prediction

Objective:
Predict the selling price of used cars based on features like brand, year, mileage, fuel type, transmission, and ownership.

Dataset:

• 301 entries with columns: Car_Name, Year, Selling_Price, Present_Price, Driven_kms, Fuel_Type, Selling_type, Transmission, Owner.

Workflow:

1. Data Loading & Cleaning: Handled missing values and cleaned columns.
2. Feature Engineering: Converted categorical features using one-hot encoding.
3. Model Preparation: Defined features (X) and target (y = Selling_Price); train-test split (80/20).
4. Model Training: LinearRegression from scikit-learn.
5. Prediction & Evaluation: MAE = 2.04, R² = 0.6; scatter plots for actual vs predicted prices.
6. Visualization: Explored feature impact on prices.

Insights:

• Present_Price, Year, mileage, and fuel type significantly affect selling price.
• Useful for dealer valuations, online marketplaces, or financial assessments.

Files:

• CodeAlpha_Car_Price_Prediction/car_price_prediction.py — Python code
• CodeAlpha_Car_Price_Prediction/car_data.csv — Dataset
• CodeAlpha_Car_Price_Prediction/CAR PRICE PREDICTION (REPORT).pdf — Report

---

3. CodeAlpha_Unemployment_Analysis

Objective:
Analyze unemployment trends in India, identify seasonal patterns, and examine Covid-19 impact.

Datasets:

1. CodeAlpha_Unemployment_Analysis/Unemployment in India.csv — historical employment data
2. CodeAlpha_Unemployment_Analysis/Unemployment_Rate_upto_11_2020.csv — monthly unemployment rates up to Nov 2020

Workflow:

1. Data Loading & Cleaning: Converted date columns and removed missing values.
2. Exploration & Visualization: Statistical summaries, time-series plots, boxplots for seasonal trends.
3. Covid-19 Analysis: Compared pre-Covid and during-Covid unemployment; average rate rose from 9.23% → 12.96%.
4. Insights Generation: Identified spikes during pandemic and recurring seasonal trends.

Insights:

• Sharp rise in unemployment during Covid-19.
• Seasonal patterns highlight temporary/informal employment effects.
• Results can guide policy and economic planning.

Files:

• CodeAlpha_Unemployment_Analysis/unemployment_analysis.py — Python code
• CodeAlpha_Unemployment_Analysis/Unemployment in India.csv — Dataset
• CodeAlpha_Unemployment_Analysis/Unemployment_Rate_upto_11_2020.csv — Dataset
• CodeAlpha_Unemployment_Analysis/EMPLOYMENT ANALYSIS (REPORT).pdf — Report

---

How to Use

1. Clone the repository:

   git clone https://github.com/RafayImraan/codealpha_tasks.git