Tasks Overview

Task 1 (Visualization of genders and age using Histogram)

This script visualizes the distribution of genders and ages in a population using bar charts and histograms with matplotlib.

Overview :

• Bar Chart for Genders: Displays the distribution of gender categories ('Male', 'Female', 'Other') using a bar chart.
• Histogram for Ages: Shows the distribution of ages in the population using a histogram with specified bins.

Usage : Run the script to generate visual representations of the given gender and age data.

Requirements :

• matplotlib library for plotting the graphs.

Task 2 (Titanic Dataset Analysis)

This script performs data cleaning and exploratory data analysis (EDA) on the Titanic dataset using pandas, matplotlib, and seaborn.

Overview :

• Data Cleaning: Handles missing values by dropping the 'Cabin' column, filling missing 'Age' values with the median age, and filling missing 'Embarked' values with the mode.

• Exploratory Data Analysis:

  • Provides summary statistics of the dataset.
  • Visualizes survival rates by gender, passenger class, and embarkation point.
  • Analyzes age and fare distributions.
  • Displays a correlation heatmap of numerical features.

• Visualizations:

  • Survival by Gender: Count plot showing the number of survivors and non-survivors by gender.
  • Survival by Passenger Class: Count plot showing the number of survivors and non-survivors by passenger class.
  • Age Distribution: Histogram with a kernel density estimate (KDE) showing the distribution of ages.
  • Fare Distribution: Histogram with a KDE showing the distribution of fares.
  • Survival by Embarkation Point: Count plot showing the number of survivors and non-survivors by embarkation point.
  • Correlation Heatmap: Heatmap displaying the correlations between numerical features.

Requirements

• pandas for data manipulation.
• matplotlib and seaborn for data visualization.

Usage

• Load the Titanic dataset as a CSV file named titanic.csv.
• Run the script to clean the data and generate visualizations.

Task 3 (Bank Marketing Prediction)

This project uses a decision tree classifier to predict whether a client will subscribe to a term deposit based on the UCI Bank Marketing dataset.

Overview

• Data Source: UCI Bank Marketing Dataset
• Data Preprocessing:
  • Categorical variables are encoded using one-hot encoding.
  • The target variable 'y' is converted from 'yes'/'no' to binary (1/0).

Steps

1. Data Loading: Load the dataset directly from the UCI repository.
2. Encoding: Apply one-hot encoding to categorical variables.
3. Feature and Target Split: Separate the data into features (X) and target (y).
4. Train-Test Split: Split the data into training and testing sets (80% train, 20% test).
5. Model Training: Train a decision tree classifier on the training data.
6. Prediction: Make predictions on the testing data.
7. Evaluation:
   • Calculate accuracy of the model.
   • Generate a classification report with precision, recall, and F1-score.
8. Visualization: Plot the decision tree to visualize the model's decision-making process.

Results

• Accuracy: The accuracy of the decision tree classifier on the test data.
• Classification Report: Detailed performance metrics including precision, recall, and F1-score.

Requirements

• pandas for data manipulation.
• scikit-learn for machine learning and model evaluation.
• matplotlib for plotting the decision tree.

Usage

• Run the script to load the data, preprocess it, train the model, and evaluate its performance.
• Visualize the decision tree to understand the model's decisions.

Task 4 (Social Media Sentiment Analysis)

This project analyzes the sentiment of social media posts using the VADER (Valence Aware Dictionary and sEntiment Reasoner) sentiment analysis tool from NLTK. The analysis includes sentiment distribution, sentiment trends over time, and visualization of common words in positive and negative posts.

Overview

• Data Source: A CSV file containing social media posts with 'text' and 'timestamp' columns.
• Sentiment Analysis: Utilizes the VADER sentiment analyzer to compute sentiment scores for each post.
• Visualizations:
  • Sentiment distribution histogram.
  • Sentiment trends over time.
  • Word clouds for positive and negative sentiments.

Steps

1. Data Loading: Load the dataset containing social media posts.
2. Sentiment Analysis: Apply the VADER sentiment analyzer to each post to obtain sentiment scores.
3. Visualization:
   • Sentiment Distribution: Plot a histogram of sentiment scores.
   • Sentiment Trends Over Time: Calculate and plot the average sentiment score over time.
   • Word Clouds: Generate word clouds for positive and negative sentiment posts.

Requirements

• pandas for data manipulation.
• nltk for sentiment analysis.
• matplotlib and seaborn for data visualization.
• wordcloud for generating word clouds.

Usage

• Ensure the dataset is named your_dataset.csv and contains 'text' and 'timestamp' columns.
• Run the script to perform sentiment analysis and generate visualizations.