The analysis of Microsoft’s stock performance reveals steady growth with fluctuations, providing valuable insights for future trend analysis. Technical indicators such as moving averages, RSI, Bollinger Bands, and MACD help identify trends, volatility levels, and potential buy/sell opportunities. Trading volume analysis shows a strong correlation between spikes in volume and price movements, emphasizing its role in market sentiment.

The post-pandemic surge in trading activity highlights the impact of external events on stock behavior. With a CAGR of 34.01% and a Sharpe Ratio of 0.075, Microsoft demonstrates strong long-term growth with moderate risk. Additionally, Random Forest outperforms Linear Regression in predictive accuracy, making it the preferred model for forecasting stock movements. This project leverages machine learning and financial analytics to provide data-driven insights, helping investors optimize strategies, manage risks, and enhance decision-making.

The Average Global CO2 Emissions Per Capita (1990-2020) graph highlights key trends in global emissions over the years. Emissions declined in the 1990s, followed by a sharp rise from 2000 to 2008 due to industrial growth. A significant drop in 2009 aligns with the global financial crisis, while 2010-2014 saw relative stability.

From 2015 onwards, emissions steadily declined, with a sharp drop in 2020, likely due to the COVID-19 pandemic and a global shift towards cleaner energy. These trends reflect the impact of economic events, policy changes, and sustainability efforts on global CO2 emissions.

The predictive model identifies key factors driving credit card churn, including low transaction count, low revolving balance, limited banking relationships, inactivity, and unresolved customer issues. Customers with fewer transactions and lower balances are more likely to leave, so offering cashback, discounts, and promotions can boost engagement. Attrition is highest among those using only one or two bank products, highlighting the need for better service transparency and customer support.

Female customers, who make higher transactions but have lower credit limits, can be retained by increasing their credit limits. Inactivity beyond 2-4 months significantly contributes to churn, making automated engagement messages essential. Additionally, frequent customer interactions without resolution indicate dissatisfaction, necessitating a robust feedback system to enhance customer support. Implementing these strategies will help banks reduce churn, improve retention, and boost customer engagement effectively.

The SVM model demonstrates strong performance in identifying unfit water samples, correctly classifying 408 cases. However, 192 unfit samples were misclassified as potable, posing a potential health risk, while 139 potable samples were incorrectly labeled as unfit, leading to unnecessary water rejection. These misclassifications highlight the model's predictive tendencies and areas of imbalance in classification.

The tuned model achieved an accuracy of 98% on training data and 96.8% on test data, demonstrating reliable predictive performance. The Random Forest model identified Lead Time, Number of Special Requests, Market Segment Type (Online), Average Price per Room, and Arrival Month as the most significant factors influencing cancellations, while other variables had minimal impact.