data-science-coding-exercise

Data Scientist Coding Exercise

Preparation of the Assessment Environment

• All coding exercises have been verified using Google Colab https://colab.research.google.com/
• It is suggested that you as well use Google Colab but please feel free to use whatever platform or coding environment you feel most comfortable with.
• Ensure that your coding platform (e.g., Google Colab) is set up and fully functional.
• Confirm that the development environment has the necessary libraries pre-installed (e.g., Python, pandas, scikit-learn, Jupyter Notebook).
• Download the repository

git clone https://github.com/stordco/data-science-coding-exercise.git

• There are 3 problem statments below. You are expected to develop the code that fulfills the requirements and provide the desired outputs.
• There are 3 CSV files are included in this repository that will be needed for this exercise as well.
• You will have 48 hours to complete the assignment.
• Once completed, please share your google colab notebook and outputs or zip up the code and outputs. Email it to the Stord/Maxima talent acquisition team member that you have been working with.
• If you have any questions, please send them via email to the Stord/Maxima talent acquisition team member that you have been working with.

Statistical Inference

Model Building and Evaluation

• You are provided with a dataset from a marketing campaign. The dataset includes user_id, age, income, previous_purchases, and purchase (1 if the user made a purchase, 0 otherwise).
• Your task is to build a logistic regression model to predict whether a user will make a purchase based on the provided features.

Instructions

1. Load the dataset.
2. Split the dataset into training and testing sets.
3. Train a logistic regression model on the training set.
4. Evaluate the model on the testing set using accuracy, precision, recall, and F1 score.
5. Output the evaluation metrics.

Expected Output Accuracy, precision, recall, and F1 score

Dataset

https://github.com/stordco/data-science-coding-exercise/blob/e8ee79cec4067fef8a32ba7aeb9c4ea17087a8a5/marketing_campaign-stat-inf-q3-hard.csv

General Programming

Model Building and Data Analysis

• You are given a dataset of customer transactions in a CSV file.
• The dataset includes transaction_id, customer_id, transaction_amount, and transaction_date.
• Your task is to write a function that reads the dataset and returns the total transaction amount for each customer.

Instructions

1. Write a function named total_transaction_amount_per_customer.
2. The function should take a file path as input.
3. Read the CSV file into a pandas DataFrame.
4. Group the data by customer_id and calculate the total transaction amount for each customer.
5. Return a dictionary where the keys are customer_id and the values are the total transaction amounts.

Expected Output A dictionary with customer IDs and their corresponding total transaction amounts.

Dataset

https://github.com/stordco/data-science-coding-exercise/blob/54f77ac0e37b1e4f7ce46bc66397ce0b7dad8c3f/customer_transactions_small.csv

Predictive Analytics

Model Building and Hyperparameter Tuning

• You are given a dataset containing customer churn information for a telecommunications company.
• The dataset includes customer_id, tenure, monthly_charges, total_charges, contract_type, and churn (1 if the customer churned, 0 otherwise).
• Your task is to build a logistic regression model to predict customer churn and perform hyperparameter tuning using cross-validation.

Instructions

1. Load the dataset.
2. Perform any necessary data cleaning and feature engineering.
3. Split the dataset into training and testing sets.
4. Train a logistic regression model and use cross-validation to find the best hyperparameters.
5. Evaluate the final model on the testing set using accuracy, precision, recall, and F1 score.

Expected Output Accuracy, precision, recall, and F1 score for the test set

Dataset

https://github.com/stordco/data-science-coding-exercise/blob/9ec469ef3f73609f7d0923d9ac8e234863e482f8/customer_churn-predict-model-q3-hard.csv