Dtata Analyis

1. How AI Can Help in Data Analysis
• A quick introduction with Python
• Powered by Pandas & Scikit-Learn

2. What is AI in Data Analysis?
• - Enables pattern discovery
• - Helps with classification, prediction
• - Saves time and improves accuracy

3. Real-World Applications
• - Finance: Fraud detection
• - Retail: Customer segmentation
• - Healthcare: Risk prediction
• - Marketing: Sentiment analysis

4. Typical Workflow
• 1. Load data
• 2. Clean and preprocess
• 3. Apply AI model
• 4. Visualize results

5. Step 1: Load Data
• Use Pandas to load and preview datasets from
CSV/Excel

6. Python Code: Load Data
• import pandas as pd
• data = pd.read_csv('data.csv')
• data.head()

7. Step 2: Preprocess
• - Handle missing data
• - Encode categorical values
• - Normalize or scale data

8. Python Code: Preprocess
• data.fillna(0, inplace=True)
• data['category'] =
data['category'].astype('category').cat.codes

9. Step 3: Train AI Model
• Use machine learning models like Linear
Regression, Decision Trees, etc.

10. Python Code: Train Model
• from sklearn.linear_model import
LinearRegression
• model = LinearRegression()
• model.fit(X, y)

11. Step 4: Predict
• Make predictions based on trained model
• Useful for forecasting, recommendations

12. Python Code: Prediction
• predicted = model.predict([[5]])
• print(predicted)

13. Visualizing Results
• Use matplotlib or seaborn for insights via
graphs

14. Python Code: Visualization
• import matplotlib.pyplot as plt
• data.plot(kind='scatter', x='hours', y='score')
• plt.show()

15. Summary
• - AI + Python = Smarter Analysis
• - Easy to use libraries
• - Great for automation & insight discovery