Data Science Tutorial Articles - No AI

Project Title: Write 20 Engaging Use Case-Based Tutorial Articles in Data Science (No AI Content) Description: We are looking for an experienced writer to create 20 engaging, use case-driven, and search engine-optimized tutorial articles on specific, real-world data science applications. The content must be manually written without AI assistance, well-researched, and highly practical, appealing to both beginners and intermediate-level data science enthusiasts. Project Requirements: [login to view URL] (Each article should focus on solving a specific problem): - Predicting Customer Churn Using Logistic Regression - Sentiment Analysis for Movie Reviews Using NLP Techniques - Fraud Detection in Financial Transactions with Random Forest - Optimizing Delivery Routes with K-Means Clustering - Forecasting Stock Prices Using ARIMA Models - Detecting Fake News with Naive Bayes Classifier - Building a Job Recommendation System with Collaborative Filtering - Image Classification for Medical Diagnosis Using CNNs - Real-Time Anomaly Detection in IoT Sensor Data - Designing a Chatbot for Customer Support with Rasa Framework - Automating Email Spam Detection with Machine Learning - Predictive Maintenance in Manufacturing Using Time Series Analysis - Demand Forecasting for Retail Inventory Optimization - Identifying Customer Segments for Marketing Campaigns with Hierarchical Clustering - Generating Synthetic Data to Augment Machine Learning Models - Predicting House Prices Using Gradient Boosting Techniques - Detecting and Classifying Defects in Factory Products with Computer Vision - Building a Sentiment Dashboard Using Plotly and Dash - Deploying a Machine Learning Model as a Web Service Using Flask - Evaluating and Mitigating Bias in AI Models Using Explainable AI Tools 2. Article Specifications: - Word count: 1200–1500 words per article. - Each article should focus on a specific, real-world use case and guide the reader step-by-step on how to solve it. - Include relevant code snippets (preferably in Python or R), datasets (mention freely available sources), and clear visuals (charts, graphs, or screenshots). - Provide practical tips, challenges faced, and solutions for implementing the use case. - Ensure content is optimized for search engines, with appropriate keywords, meta descriptions, and headings. 3. Deliverables: - High-quality, plagiarism-free content with references for datasets and tools used. - Tutorials must follow a structured approach: Introduction, Problem Definition, Dataset Overview, Implementation, Results, and Conclusion. - Articles should inspire hands-on learning, encouraging readers to apply the concepts themselves. Additional Note: We are committed to original, high-quality educational content. AI-generated articles will not be accepted. The selected writer will be required to confirm adherence to this rule. If you have expertise in data science and a passion for explaining complex concepts through practical use cases, we’d love to work with you!