This README file provides an overview of the Cognizant Artificial Intelligence Job Simulation completed on Forage in May 2024. The simulation was designed to provide hands-on experience with AI-related tasks relevant to Cognizant's Data Science team. The main activities included exploratory data analysis, model training, and presenting findings to the business.

• Conduct exploratory data analysis (EDA) for a technology-led client, Gala Groceries.
• Prepare a Python module for model training and performance evaluation.
• Communicate findings and analysis through a PowerPoint presentation.

Tools Used:

• Python
• Google Colab

Description: Conducted EDA on a dataset provided by Gala Groceries to uncover patterns, insights, and relationships within the data. This involved:

• Cleaning and preprocessing the data.
• Visualizing data distributions and relationships.
• Identifying key features and potential areas for further analysis.

Key Steps:

• Imported necessary libraries (pandas, numpy, matplotlib, seaborn).
• Loaded and explored the dataset.
• Performed data cleaning (handling missing values, outlier detection).
• Generated summary statistics.
• Created visualizations (histograms, scatter plots, correlation matrix).

Tools Used:

• Python
• scikit-learn

Description: Prepared a Python module to train a machine learning model and evaluate its performance. This module is intended for the Machine Learning engineering team at Cognizant.

Key Steps:

• Split the data into training and test sets.
• Selected and trained a machine learning model (e.g., linear regression, decision tree).
• Evaluated the model using appropriate metrics (e.g., accuracy, precision, recall, F1 score).
• Output the performance metrics for review.

Sample Code:

from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, classification_report

# Load data
data = pd.read_csv('gala_groceries_data.csv')

# Data preprocessing
# ... (include data cleaning steps here)

# Split data
X = data.drop('target', axis=1)
y = data['target']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Train model
model = RandomForestClassifier(random_state=42)
model.fit(X_train, y_train)

# Predict and evaluate
y_pred = model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
report = classification_report(y_test, y_pred)

print(f'Accuracy: {accuracy}')
print('Classification Report:')
print(report)

Tools Used:

• Microsoft PowerPoint

Description: Compiled the findings and analysis into a PowerPoint presentation to communicate the results back to the business stakeholders at Gala Groceries.

Key Steps:

• Summarized the EDA findings, highlighting key insights and patterns.
• Described the model training process and performance metrics.
• Provided actionable recommendations based on the analysis.
• Created visual aids to support the presentation (charts, graphs, tables).

The Cognizant Artificial Intelligence Job Simulation on Forage provided a comprehensive experience in data analysis, model training, and business communication. The skills and insights gained from this simulation are directly applicable to real-world AI projects and align with the objectives of Cognizant’s Data Science team.

• eda_notebook.ipynb: Jupyter notebook containing the exploratory data analysis.
• model_training.py: Python module for model training and performance evaluation.
• findings_presentation.pptx: PowerPoint presentation summarizing the findings and recommendations.

This README file provides a structured summary of the activities and outcomes of the job simulation, ensuring that all relevant details are clearly documented for future reference.