Data Science Mini-Projects

This repository started as a way to apply what I learned about data science to a random dataset delivered to my inbox each week by Jeremy Singer-Vine at DataIsPlural.

The following template is what I use for each project, and each mini-project contains a jupyter notebook, a README containing the answers to these questions, and other items I found useful when finding and solving puzzles in the data.

Data Science Project Template

1. Inspecting the Dataset:

a. Initial Glance:

• Load the dataset.
• View the first few rows to understand the structure.
• Check the number of rows, columns, and data types.

b. Summary Statistics:

• For numerical data: mean, median, min, max, standard deviation.
• For categorical data: unique values, count of each value.

c. Missing Values:

• Check for any missing values.
• Note down columns with a high percentage of missing values.

d. Visualization:

• Histograms for numerical data.
• Bar plots for categorical data.
• Correlation plots or heatmaps for numerical data.

2. Identify Potential Problems or Questions:

a. Classification:

• Is there a categorical column that can be predicted based on other features?
• Example: Predicting if a person will default on a loan.

b. Regression:

• Is there a numerical column that can be predicted?
• Example: Predicting house prices based on features.

c. Clustering:

• Are there any interesting patterns or groups in the data?
• Example: Customer segmentation.

d. Anomaly Detection:

• Are there any outliers or anomalies in the data?
• Example: Detecting fraudulent transactions.

3. Data Preprocessing:

a. Handle Missing Data:

• Impute or drop based on the nature of the dataset.

b. Feature Engineering:

• Create new features that might be helpful.
• Example: Extracting day, month, year from a date column.

c. Categorical Data:

• Convert categorical data into numerical format using encoding techniques like One-Hot Encoding or Label Encoding.

d. Scaling:

• Standardize or normalize data if necessary.

4. Modeling:

a. Choose a Relevant Algorithm:

• Based on the problem (classification, regression, clustering).

b. Train/Test Split:

• Split the data into training and testing sets.

c. Model Training:

• Train the model on the training data.

d. Model Evaluation:

• Evaluate the model using appropriate metrics.
• For classification: accuracy, precision, recall, F1 score, ROC curve.
• For regression: MSE, RMSE, MAE, R^2.

e. Hyperparameter Tuning (Optional):

• Use techniques like Grid Search or Random Search to find the best parameters.

5. Documenting Findings:

a. Introduction:

• Briefly describe the dataset and the problem.

b. Data Exploration:

• Include visualizations and summary statistics.

c. Data Preprocessing:

• Describe steps taken and why.

d. Modeling:

• Describe the algorithm, hyperparameters, and evaluation metrics.

e. Conclusion:

• Summarize the results and any potential improvements.