Step-by-Step Data Analysis and Machine Learning Workflow with Pandas, Matplotlib, and Scikit-learn

1. Data Loading and Preview Scenario: Load data from a CSV file and view the first few rows.

import pandas as pd

# Load data

df = pd.read_csv('data.csv')

# View first 5 rows

print(df.head())

2. Data Cleaning: Missing Value Handling Scenario: Fill missing values in the 'Age' column with the column mean.

mean_age = df['Age'].mean()

df['Age'].fillna(mean_age, inplace=True)

3. Data Filtering Scenario: Select rows where age is greater than 30.

filtered_df = df[df['Age'] > 30]

4. Grouping and Aggregation Scenario: Compute the average age for each gender.

grouped = df.groupby('Gender')['Age'].mean()

print(grouped)

5. Data Visualization: Bar Chart Scenario: Plot a bar chart of user counts by gender.

import matplotlib.pyplot as plt

gender_counts = df['Gender'].value_counts()

gender_counts.plot(kind='bar')

plt.title('User Count by Gender')

plt.xlabel('Gender')

plt.ylabel('Count')

plt.show()

6. Correlation Analysis Scenario: Compute Pearson correlation coefficients between numeric variables.

correlation_matrix = df.corr()

print(correlation_matrix)

7. Time Series Analysis Scenario: Plot monthly sales from a date column.

df['Date'] = pd.to_datetime(df['Date']) # assume a date column exists

df.set_index('Date', inplace=True)

monthly_sales = df['Sales'].resample('M').sum()

monthly_sales.plot()

plt.title('Monthly Sales')

plt.xlabel('Month')

plt.ylabel('Sales')

plt.show()

8. Outlier Detection Scenario: Identify outliers in the 'Age' column using the IQR method.

Q1 = df['Age'].quantile(0.25)

Q3 = df['Age'].quantile(0.75)

IQR = Q3 - Q1

lower_bound = Q1 - 1.5 * IQR

upper_bound = Q3 + 1.5 * IQR

outliers = df[(df['Age'] < lower_bound) | (df['Age'] > upper_bound)]

print(outliers)

9. Simple Linear Regression Scenario: Analyze the relationship between advertising spend and sales.

from sklearn.model_selection import train_test_split

from sklearn.linear_model import LinearRegression

X = df[['Advertising_Spend']]

y = df['Sales']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

model = LinearRegression()

model.fit(X_train, y_train)

# Predict

predictions = model.predict(X_test)

10. Classification Task: Logistic Regression Scenario: Predict whether a user will purchase a product based on features.

from sklearn.linear_model import LogisticRegression

from sklearn.preprocessing import StandardScaler

# Assume we have features X and target y

X = df[['Age', 'Income', 'Gender']]

y = df['Will_Purchase']

scaler = StandardScaler()

X_scaled = scaler.fit_transform(X)

X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)

logreg = LogisticRegression()

logreg.fit(X_train, y_train)

# Predict probabilities

purchase_probabilities = logreg.predict_proba(X_test)