| Analyze a Zomato Dataset and Create Visualizations |
Choose a publicly available dataset and perform exploratory data analysis (EDA) to uncover insights and trends. |
- Load and clean the dataset (handle missing values, outliers, etc.).
- Perform descriptive statistics to summarize the data..
- Create visualizations like histograms, bar charts, scatter plots, and correlation matrices to explore relationships between variables.
- Present your findings in a well-documented report or Jupyter Notebook.
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Python, Pandas, Matplotlib, Seaborn, and Jupyter Notebook.
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| Loan Approval Prediction: Case Study |
Develop a machine learning model to predict outcomes based on historical data. |
- Select a dataset with a clear target variable (e.g., housing prices, customer churn, or loan default).
- Preprocess the data (handle categorical variables, normalize features, split into training and test sets).
- Train a model using algorithms like linear regression, decision trees, or random forests.
- Evaluate the model's performance using metrics such as accuracy, precision, recall, or RMSE (Root Mean Squared Error).
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Python, Scikit-learn, Pandas, NumPy, Matplotlib/Seaborn.
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| Analysis on Social Media Data (Instgram) |
Analyze the sentiment of text data, such as tweets or product reviews, to determine whether the sentiment is positive, negative, or neutral. |
- Clean and preprocess the text data (remove stopwords, punctuation, perform tokenization).
- Use a machine learning model or a pre-trained model (like VADER or BERT) to classify the sentiment of the text.
- Analyze the results and create visualizations to show the distribution of sentiments.
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Python, NLTK or SpaCy for text processing, Scikit-learn, Tweepy (for data collection), and Matplotlib/Seaborn for visualization.
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