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User Retention Analytics Project

Project Overview

This project analyzes user retention and churn behavior for a simulated online learning platform.
Using Python and data analytics techniques, the project explores engagement patterns and builds a predictive churn model.

Objectives

  • Simulate user activity data
  • Analyze cohort-based retention
  • Explore engagement metrics
  • Build a churn prediction model

Dataset Features

  • user_id
  • signup_date
  • lessons_completed
  • weekly_sessions
  • churn_probability
  • churned

Technologies Used

  • Python
  • Pandas
  • NumPy
  • Matplotlib
  • Scikit-learn

Analysis Performed

Visualizations

Cohort Retention Analysis

Retention rates were calculated based on signup month cohorts.

Cohort Retention

Engagement vs Churn

User engagement metrics were compared with churn outcomes.

Engagement Scatter

Churn Prediction Model

A logistic regression model was trained using:

  • Lessons completed
  • Weekly sessions

Model Accuracy: 0.71

Example prediction:

User behavior:

  • Lessons completed: 3
  • Weekly sessions: 1

Predicted churn probability:54.45%

Project Structure

Retention_Analytics_Project

  1. project2.py
  2. user_dataset.csv
  3. README.md
  4. visuals 4.1 cohort_retention.png 4.2engagement_scatter.png
  5. report

Key Insights

  • Higher weekly engagement reduces churn risk
  • Cohort retention varies across signup periods
  • Logistic regression provides a baseline churn prediction model

About

User retention analysis and churn prediction using Python, cohort analysis and logistic regression.

Topics

churn-predictiondata-analysismachine-learningpandaspythonretention-analysisscikit-learn

Resources

Readme
Activity

Stars

0 stars

Watchers

0 watching

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0 forks
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var __re = new RegExp('^' + "github\\.com" + '

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Repository files navigation
User Retention Analytics Project
Project Overview
This project analyzes user retention and churn behavior for a simulated online learning platform.

Using Python and data analytics techniques, the project explores engagement patterns and builds a predictive churn model.
Objectives
  • Simulate user activity data
  • Analyze cohort-based retention
  • Explore engagement metrics
  • Build a churn prediction model
Dataset Features
  • user_id
  • signup_date
  • lessons_completed
  • weekly_sessions
  • churn_probability
  • churned
Technologies Used
  • Python
  • Pandas
  • NumPy
  • Matplotlib
  • Scikit-learn
Analysis Performed
Visualizations
Cohort Retention Analysis
Retention rates were calculated based on signup month cohorts.
Cohort Retention
Engagement vs Churn
User engagement metrics were compared with churn outcomes.
Engagement Scatter
Churn Prediction Model
A logistic regression model was trained using:
  • Lessons completed
  • Weekly sessions
Model Accuracy: 0.71
Example prediction:
User behavior:
  • Lessons completed: 3
  • Weekly sessions: 1
Predicted churn probability:54.45%
Project Structure
Retention_Analytics_Project
  1. project2.py
  2. user_dataset.csv
  3. README.md
  4. visuals
4.1 cohort_retention.png
4.2engagement_scatter.png
  5. report
Key Insights
  • Higher weekly engagement reduces churn risk
  • Cohort retention varies across signup periods
  • Logistic regression provides a baseline churn prediction model
About
User retention analysis and churn prediction using Python, cohort analysis and logistic regression.
Topics
churn-predictiondata-analysismachine-learningpandaspythonretention-analysisscikit-learn
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
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user_dataset.csv
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Repository files navigation
User Retention Analytics Project
Project Overview
This project analyzes user retention and churn behavior for a simulated online learning platform.

Using Python and data analytics techniques, the project explores engagement patterns and builds a predictive churn model.
Objectives
  • Simulate user activity data
  • Analyze cohort-based retention
  • Explore engagement metrics
  • Build a churn prediction model
Dataset Features
  • user_id
  • signup_date
  • lessons_completed
  • weekly_sessions
  • churn_probability
  • churned
Technologies Used
  • Python
  • Pandas
  • NumPy
  • Matplotlib
  • Scikit-learn
Analysis Performed
Visualizations
Cohort Retention Analysis
Retention rates were calculated based on signup month cohorts.
Cohort Retention
Engagement vs Churn
User engagement metrics were compared with churn outcomes.
Engagement Scatter
Churn Prediction Model
A logistic regression model was trained using:
  • Lessons completed
  • Weekly sessions
Model Accuracy: 0.71
Example prediction:
User behavior:
  • Lessons completed: 3
  • Weekly sessions: 1
Predicted churn probability:54.45%
Project Structure
Retention_Analytics_Project
  1. project2.py
  2. user_dataset.csv
  3. README.md
  4. visuals
4.1 cohort_retention.png
4.2engagement_scatter.png
  5. report
Key Insights
  • Higher weekly engagement reduces churn risk
  • Cohort retention varies across signup periods
  • Logistic regression provides a baseline churn prediction model
About
User retention analysis and churn prediction using Python, cohort analysis and logistic regression.
Topics
churn-predictiondata-analysismachine-learningpandaspythonretention-analysisscikit-learn
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
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, 'i');
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// Highlight search terms from Google/DuckDuckGo/Bing referrer
(function() {
var ref = document.referrer;
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highlight(document.body);
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observer.observe(document.body, { childList: true, subtree: true });
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} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
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try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Repository files navigation
User Retention Analytics Project
Project Overview
This project analyzes user retention and churn behavior for a simulated online learning platform.

Using Python and data analytics techniques, the project explores engagement patterns and builds a predictive churn model.
Objectives
  • Simulate user activity data
  • Analyze cohort-based retention
  • Explore engagement metrics
  • Build a churn prediction model
Dataset Features
  • user_id
  • signup_date
  • lessons_completed
  • weekly_sessions
  • churn_probability
  • churned
Technologies Used
  • Python
  • Pandas
  • NumPy
  • Matplotlib
  • Scikit-learn
Analysis Performed
Visualizations
Cohort Retention Analysis
Retention rates were calculated based on signup month cohorts.
Cohort Retention
Engagement vs Churn
User engagement metrics were compared with churn outcomes.
Engagement Scatter
Churn Prediction Model
A logistic regression model was trained using:
  • Lessons completed
  • Weekly sessions
Model Accuracy: 0.71
Example prediction:
User behavior:
  • Lessons completed: 3
  • Weekly sessions: 1
Predicted churn probability:54.45%
Project Structure
Retention_Analytics_Project
  1. project2.py
  2. user_dataset.csv
  3. README.md
  4. visuals
4.1 cohort_retention.png
4.2engagement_scatter.png
  5. report
Key Insights
  • Higher weekly engagement reduces churn risk
  • Cohort retention varies across signup periods
  • Logistic regression provides a baseline churn prediction model
About
User retention analysis and churn prediction using Python, cohort analysis and logistic regression.
Topics
churn-predictiondata-analysismachine-learningpandaspythonretention-analysisscikit-learn
Resources
Readme
Activity
Stars
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Watchers
0 watching
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'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',
'ref', 'ref_src', 'source', 'medium', 'campaign'];
function cleanUrl(url) {
try {
var u = new URL(url, window.location.origin);
var changed = false;
trackingParams.forEach(function(p) {
if (u.searchParams.has(p)) {
u.searchParams.delete(p);
changed = true;
}
});
return changed ? u.toString() : url;
} catch (e) {
return url;
}
}
function cleanLinks() {
document.querySelectorAll('a[href]').forEach(function(a) {
var clean = cleanUrl(a.href);
if (clean !== a.href) a.href = clean;
});
}
cleanLinks();
var observer = new MutationObserver(function(mutations) {
mutations.forEach(function(m) {
m.addedNodes.forEach(function(node) {
if (node.nodeType === 1) {
if (node.tagName === 'A') cleanLinks();
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if (clean !== a.href) a.href = clean;
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observer.observe(document.body, { childList: true, subtree: true });
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} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
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user_dataset.csv
user_dataset.csv
Repository files navigation
User Retention Analytics Project
Project Overview
This project analyzes user retention and churn behavior for a simulated online learning platform.

Using Python and data analytics techniques, the project explores engagement patterns and builds a predictive churn model.
Objectives
  • Simulate user activity data
  • Analyze cohort-based retention
  • Explore engagement metrics
  • Build a churn prediction model
Dataset Features
  • user_id
  • signup_date
  • lessons_completed
  • weekly_sessions
  • churn_probability
  • churned
Technologies Used
  • Python
  • Pandas
  • NumPy
  • Matplotlib
  • Scikit-learn
Analysis Performed
Visualizations
Cohort Retention Analysis
Retention rates were calculated based on signup month cohorts.
Cohort Retention
Engagement vs Churn
User engagement metrics were compared with churn outcomes.
Engagement Scatter
Churn Prediction Model
A logistic regression model was trained using:
  • Lessons completed
  • Weekly sessions
Model Accuracy: 0.71
Example prediction:
User behavior:
  • Lessons completed: 3
  • Weekly sessions: 1
Predicted churn probability:54.45%
Project Structure
Retention_Analytics_Project
  1. project2.py
  2. user_dataset.csv
  3. README.md
  4. visuals
4.1 cohort_retention.png
4.2engagement_scatter.png
  5. report
Key Insights
  • Higher weekly engagement reduces churn risk
  • Cohort retention varies across signup periods
  • Logistic regression provides a baseline churn prediction model
About
User retention analysis and churn prediction using Python, cohort analysis and logistic regression.
Topics
churn-predictiondata-analysismachine-learningpandaspythonretention-analysisscikit-learn
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
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, 'i');
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// Auto-enable theater mode on YouTube
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var btn = document.querySelector('button[aria-label="Theater mode"], ytd-player #player button[title="Theater mode"]');
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}, 1000);
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Repository files navigation
User Retention Analytics Project
Project Overview
This project analyzes user retention and churn behavior for a simulated online learning platform.

Using Python and data analytics techniques, the project explores engagement patterns and builds a predictive churn model.
Objectives
  • Simulate user activity data
  • Analyze cohort-based retention
  • Explore engagement metrics
  • Build a churn prediction model
Dataset Features
  • user_id
  • signup_date
  • lessons_completed
  • weekly_sessions
  • churn_probability
  • churned
Technologies Used
  • Python
  • Pandas
  • NumPy
  • Matplotlib
  • Scikit-learn
Analysis Performed
Visualizations
Cohort Retention Analysis
Retention rates were calculated based on signup month cohorts.
Cohort Retention
Engagement vs Churn
User engagement metrics were compared with churn outcomes.
Engagement Scatter
Churn Prediction Model
A logistic regression model was trained using:
  • Lessons completed
  • Weekly sessions
Model Accuracy: 0.71
Example prediction:
User behavior:
  • Lessons completed: 3
  • Weekly sessions: 1
Predicted churn probability:54.45%
Project Structure
Retention_Analytics_Project
  1. project2.py
  2. user_dataset.csv
  3. README.md
  4. visuals
4.1 cohort_retention.png
4.2engagement_scatter.png
  5. report
Key Insights
  • Higher weekly engagement reduces churn risk
  • Cohort retention varies across signup periods
  • Logistic regression provides a baseline churn prediction model
About
User retention analysis and churn prediction using Python, cohort analysis and logistic regression.
Topics
churn-predictiondata-analysismachine-learningpandaspythonretention-analysisscikit-learn
Resources
Readme
Activity
Stars
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, 'i');
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function unstick() {
document.querySelectorAll('header, nav, [role="banner"], .header, .navbar, .sticky, .fixed-top, [style*="position: fixed"], [style*="position:sticky"]').forEach(function(el) {
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README.md
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project2.py
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requirements.txt
user_dataset.csv
user_dataset.csv
Repository files navigation
User Retention Analytics Project
Project Overview
This project analyzes user retention and churn behavior for a simulated online learning platform.

Using Python and data analytics techniques, the project explores engagement patterns and builds a predictive churn model.
Objectives
  • Simulate user activity data
  • Analyze cohort-based retention
  • Explore engagement metrics
  • Build a churn prediction model
Dataset Features
  • user_id
  • signup_date
  • lessons_completed
  • weekly_sessions
  • churn_probability
  • churned
Technologies Used
  • Python
  • Pandas
  • NumPy
  • Matplotlib
  • Scikit-learn
Analysis Performed
Visualizations
Cohort Retention Analysis
Retention rates were calculated based on signup month cohorts.
Cohort Retention
Engagement vs Churn
User engagement metrics were compared with churn outcomes.
Engagement Scatter
Churn Prediction Model
A logistic regression model was trained using:
  • Lessons completed
  • Weekly sessions
Model Accuracy: 0.71
Example prediction:
User behavior:
  • Lessons completed: 3
  • Weekly sessions: 1
Predicted churn probability:54.45%
Project Structure
Retention_Analytics_Project
  1. project2.py
  2. user_dataset.csv
  3. README.md
  4. visuals
4.1 cohort_retention.png
4.2engagement_scatter.png
  5. report
Key Insights
  • Higher weekly engagement reduces churn risk
  • Cohort retention varies across signup periods
  • Logistic regression provides a baseline churn prediction model
About
User retention analysis and churn prediction using Python, cohort analysis and logistic regression.
Topics
churn-predictiondata-analysismachine-learningpandaspythonretention-analysisscikit-learn
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
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, 'i');
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// Universal Dark Mode - works on any site
(function() {
var enabled = true;
function applyDarkMode() {
if (!enabled) return;
// Create style element if it doesn't exist
var style = document.getElementById('universal-dark-mode-style');
if (!style) {
style = document.createElement('style');
style.id = 'universal-dark-mode-style';
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}
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html {
filter: invert(1) hue-rotate(180deg) !important;
background: #1a1a2e !important;
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img, video, iframe, canvas, svg, picture, [style*="background-image"] {
filter: invert(1) hue-rotate(180deg) !important;
}
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[data-theme="light"], [data-theme="light"],
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.markdown-body pre, .markdown-body pre *,
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filter: none !important;
}
/* Fix common UI elements */
.modal, .popup, .dropdown-menu, .tooltip, .popover {
filter: invert(1) hue-rotate(180deg) !important;
background: #2d2d44 !important;
border-color: #444 !important;
}
/* Scrollbars */
::-webkit-scrollbar { background: #1a1a2e !important; }
::-webkit-scrollbar-thumb { background: #444 !important; }
::-webkit-scrollbar-thumb:hover { background: #555 !important; }
/* Selection */
::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }
::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }
';
}
function removeDarkMode() {
var style = document.getElementById('universal-dark-mode-style');
if (style) style.remove();
}
// Toggle with Alt+Shift+D
document.addEventListener('keydown', function(e) {
if (e.altKey && e.shiftKey && e.key === 'D') {
e.preventDefault();
enabled = !enabled;
if (enabled) {
applyDarkMode();
console.log('[Universal Dark Mode] Enabled');
} else {
removeDarkMode();
console.log('[Universal Dark Mode] Disabled');
}
}
});
// Apply on load
applyDarkMode();
// Re-apply on dynamic content
var observer = new MutationObserver(function(mutations) {
if (enabled && !document.getElementById('universal-dark-mode-style')) {
applyDarkMode();
}
});
observer.observe(document.head, { childList: true });
console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');
})();
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
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User Retention Analytics Project
Project Overview
This project analyzes user retention and churn behavior for a simulated online learning platform.

Using Python and data analytics techniques, the project explores engagement patterns and builds a predictive churn model.
Objectives
  • Simulate user activity data
  • Analyze cohort-based retention
  • Explore engagement metrics
  • Build a churn prediction model
Dataset Features
  • user_id
  • signup_date
  • lessons_completed
  • weekly_sessions
  • churn_probability
  • churned
Technologies Used
  • Python
  • Pandas
  • NumPy
  • Matplotlib
  • Scikit-learn
Analysis Performed
Visualizations
Cohort Retention Analysis
Retention rates were calculated based on signup month cohorts.
Cohort Retention
Engagement vs Churn
User engagement metrics were compared with churn outcomes.
Engagement Scatter
Churn Prediction Model
A logistic regression model was trained using:
  • Lessons completed
  • Weekly sessions
Model Accuracy: 0.71
Example prediction:
User behavior:
  • Lessons completed: 3
  • Weekly sessions: 1
Predicted churn probability:54.45%
Project Structure
Retention_Analytics_Project
  1. project2.py
  2. user_dataset.csv
  3. README.md
  4. visuals
4.1 cohort_retention.png
4.2engagement_scatter.png
  5. report
Key Insights
  • Higher weekly engagement reduces churn risk
  • Cohort retention varies across signup periods
  • Logistic regression provides a baseline churn prediction model
About
User retention analysis and churn prediction using Python, cohort analysis and logistic regression.
Topics
churn-predictiondata-analysismachine-learningpandaspythonretention-analysisscikit-learn
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