Built a machine learning model for customer churn prediction using Python.

From Raw Data to Insights: The Power of pandas, matplotlib & EDA Every AI project begins with one thing—data. But raw data is messy, incomplete, and often misleading. That’s where the trio of pandas, matplotlib, and EDA comes in. 🔹 pandas DataFrame gives structure—rows and columns that are easy to clean, merge, and analyze. 🔹 EDA (Exploratory Data Analysis) is the detective work—spotting missing values, outliers, and hidden trends. 🔹 matplotlib transforms numbers into visuals—so patterns are not just computed, but seen. 👉 Imagine analyzing customer churn: • pandas helps you aggregate user behavior. • EDA uncovers that churn is higher among users with late payments. • matplotlib shows the trend in a clear declining curve that business leaders can act on. Together, they turn raw data into actionable insights—the foundation for machine learning, forecasting, and business decisions. 💡 Data isn’t just numbers—it’s a story. And this trio helps you tell it right. 👉 What’s your favorite Python tool when you start exploring a new dataset? #Python #EDA #pandas #Matplotlib #DataScience #AIEngineer

🚀 Customer Feedback Text Analysis with Python I recently analyzed product reviews to understand customer sentiment and uncover what really matters to users. Here’s a quick breakdown: 🔹 Process: ▪️ Loaded review data from Excel using pandas ▪️Cleaned text with regex (lowercasing + removing special characters) ▪️Tokenized and removed stopwords ▪️Generated word frequency counts ▪️Derived sentiment from ratings (Positive / Neutral / Negative) ▪️Visualized results in Matplotlib. 🔹 Key Findings: 📊 Top Words in Reviews 🔸 good (107) 🔸 more (105) 🔸quality (86) 🔸sound (74) 🔸bass (66) 💡 Customers clearly care most about quality, sound, and bass performance. 📊 Sentiment Distribution ✅ Positive → ~80% ⚖ Neutral → ~10% ❌ Negative → small fraction 💡 Overall, reviews are strongly positive. Customers appreciate sound and bass quality, but words like battery and price also repeat often — signaling expectations in those areas.

📢 Excited to showcase my project: Churn Prediction System 🎯 🔍 Overview: This project is designed to predict which customers are most likely to discontinue a service. Using machine learning techniques, the model analyzes customer behavior and provides churn probabilities to help businesses take proactive retention measures. ⚙️ Tech Stack & Tools Used: Python (Scikit-learn, Pandas, XGBoost, NumPy) Power BI / Matplotlib (visualization) Data preprocessing & feature engineering techniques ✨ Key Highlights: Built a classification model to predict churn risk 📊 Achieved actionable insights to reduce customer attrition Developed an interactive dashboard for churn analysis 📹 Sharing a quick demo video below 👇 I’d love to hear your feedback and suggestions! #MachineLearning #DataScience #CustomerChurn #BusinessAnalytics #AI #Python #XGBoost #LinkedInProjects #FutureIntern

Excited to share my latest machine learning project where I built a model to classify Iris flowers into different species based on their physical features like sepal length, sepal width, petal length, and petal width. This classic dataset may be small, but it’s a powerful starting point for understanding how ML models work! What I did: ✅ Data Cleaning & Preprocessing – handled missing values and standardized data for accuracy. 📊 Exploratory Data Analysis (EDA) – used Seaborn & Matplotlib to visualize patterns between features. 🧠 Model Building – implemented multiple machine learning algorithms like: Logistic Regression Decision Tree Random Forest K-Nearest Neighbors (KNN) 🔍 Model Evaluation – compared performance metrics to select the most accurate model. Key Achievement: Achieved 95%+ accuracy in predicting the correct Iris species 🌱. Tech Stack: Python | Pandas | NumPy | Matplotlib | Seaborn | Scikit-learn | Jupyter Notebook https://lnkd.in/gkmEbrer

SQL Wednesday: The Unsung Backbone of Data Projects Yesterday, I talked about the importance of building projects as a path to growth. But here’s something I’ve noticed, while not every project requires SQL, almost every project that deals with structured data at scale benefits from it. SQL helps you: ✅ Store and organize your data, ✅ Query it efficiently, ✅ And prepare it for machine learning or app development. Takeaway: Even if you’ve built projects without SQL (I have too!), learning it adds an edge, it equips you to handle bigger, more data-driven projects with confidence. Question for you: How often do you bring SQL into your projects? #SQLWednesday #DataScience #Python #MachineLearning #AI #WomenInTech #STEM

Customer Spending Habits Analysis Project: In this project, I analyzed customer purchasing patterns using Python, pandas, NumPy, matplotlib, and seaborn, along with machine learning techniques (clustering & regression). Key highlights: Identified spending trends and seasonal purchase behaviors. Segmented customers into groups like high spenders, budget buyers, and occasional shoppers. Created visual insights for better understanding of customer behavior. Applied ML models for predictive analysis to support business decision-making. This analysis helps businesses improve customer retention, personalized marketing, and revenue growth through data-driven strategies. #DataScience #Python #MachineLearning #CustomerAnalysis #Clustering #DataVisualization #BusinessInsights

🚀 Excited to share my recent Machine Learning project where I worked on 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐂𝐡𝐮𝐫𝐧 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐨𝐧! The journey started with identifying the problem type — this was a 𝐜𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐩𝐫𝐨𝐛𝐥𝐞𝐦 (Will the user churn or not?). 🔎 Here’s how I approached it step by step: 1️⃣ Imported the necessary Python libraries and loaded the dataset. 2️⃣ Explored the data by asking 7 key EDA questions to understand its structure. 3️⃣ Performed 𝐄𝐃𝐀: • Plotted 𝐡𝐢𝐬𝐭𝐨𝐠𝐫𝐚𝐦𝐬 𝐚𝐧𝐝 𝐛𝐨𝐱 𝐩𝐥𝐨𝐭𝐬 for numerical data. • Visualized correlation with a 𝐡𝐞𝐚𝐭𝐦𝐚𝐩. • Used 𝐜𝐨𝐮𝐧𝐭 𝐩𝐥𝐨𝐭𝐬 for categorical data. 4️⃣ 𝐏𝐫𝐞𝐩𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠: • Filled missing values. • Encoded target labels with 𝐋𝐚𝐛𝐞𝐥𝐄𝐧𝐜𝐨𝐝𝐞𝐫. • Applied 𝐎𝐧𝐞𝐇𝐨𝐭𝐄𝐧𝐜𝐨𝐝𝐞𝐫 to categorical input features. 5️⃣ Split the dataset into 𝐭𝐫𝐚𝐢𝐧 𝐚𝐧𝐝 𝐭𝐞𝐬𝐭 𝐬𝐞𝐭𝐬. 6️⃣ Trained models using: • 𝐃𝐞𝐜𝐢𝐬𝐢𝐨𝐧 𝐓𝐫𝐞𝐞 𝐂𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐞𝐫 • 𝐑𝐚𝐧𝐝𝐨𝐦 𝐅𝐨𝐫𝐞𝐬𝐭 𝐂𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐞𝐫 7️⃣ Compared performance and selected the best model. ⚡ Result: Achieved 𝟕𝟗% 𝐚𝐜𝐜𝐮𝐫𝐚𝐜𝐲 with the RandomForestClassifier! I also evaluated the model using 𝐩𝐫𝐞𝐜𝐢𝐬𝐢𝐨𝐧 and 𝐫𝐞𝐜𝐚𝐥𝐥 to ensure balanced performance. This project gave me deeper insights into handling both numerical and categorical data, applying different visualization techniques, and selecting the right ML model for classification problems. 💡 Key takeaway: 𝐑𝐚𝐧𝐝𝐨𝐦 𝐅𝐨𝐫𝐞𝐬𝐭 not only provided the best accuracy but also showed robustness in handling feature importance and overfitting compared to a single decision tree. #MachineLearning #DataScience #CustomerChurn #RandomForest #DecisionTree #EDA #Classification #DataVisualization #Python #AI #MLProjects #DSA #SajidHameed #Projects

Day 5/100 One of the most exciting aspects of working with data is the ability to see insights rather than just compute them. Today, I explored data visualization with two of Python’s most powerful libraries: Matplotlib and Seaborn. 📊 Matplotlib Matplotlib is the backbone of Python visualization. It may be a bit verbose at times, but it offers full control over every element of a plot. Today, I worked on: -Creating line plots (basic and customized with markers, colors, and grids). -Bar plots and histograms to represent categorical and distribution data. -Scatter plots to show relationships between two variables. -Pie charts for proportions. -Building multiple plots with subplots for comparative analysis. -Even visualizing real sales data with bar graphs and time series trends. ✨ Seaborn Seaborn makes statistical visualization easier, more attractive, and concise. With just a few lines of code, I was able to create: -Categorical plots (bar, box, and violin plots). -Distribution plots (histograms, KDE plots). -Pair plots to understand multi-variable relationships in datasets. -Heatmaps for correlation analysis — one of the most powerful tools to quickly spot patterns in data. -Visualizations on the classic tips dataset and real-world sales data. 🔎 Key Insights from Today: Matplotlib is like a blank canvas 🎨 — you can paint anything, but it takes effort. Seaborn is like a designer tool 🪄 — it makes things look polished by default. Together, they form a strong foundation for data storytelling, helping analysts, engineers, and scientists make data understandable and actionable. 💡 Visualization is not just about making data look pretty. It’s about: ✔ Spotting patterns you might otherwise miss ✔ Explaining results to non-technical audiences ✔ Making data-driven decisions with confidence I’m thrilled with how much clarity these tools bring to analysis. Moving forward, I’ll be applying them in my upcoming projects to make data insights more intuitive and impactful 🚀 Github-https://lnkd.in/dguMkb3g #DataScience #MachineLearning #Python #Matplotlib #Seaborn #DataVisualization #LearningJourney #AI

🚀 Project 4: Stock Price Prediction 👉 Access the full project here: https://lnkd.in/gFFpi2Se Ever wondered if past stock movements could hint at future trends? My latest project explores a foundational approach to predicting stock prices using machine learning! This Python script demonstrates how to leverage historical stock data to build a simple yet effective Linear Regression model for predicting future closing prices. It’s a fantastic introduction to applying time series concepts to financial data, offering a transparent and interpretable prediction mechanism. At its core, the project follows a clear ML pipeline: * 📊 **Data Acquisition**: Utilizes `yfinance` to fetch real historical stock data (e.g., AAPL). * ⚙️ **Feature Engineering**: Creates "lag features" by shifting past `N` days' closing prices, transforming sequential data into a format suitable for prediction. * 🧠 **Model Training**: A `scikit-learn` Linear Regression model is trained on this prepared dataset. Crucially, the data split (`train_test_split`) is done without shuffling to preserve chronological order, which is vital for time series analysis. * 📈 **Prediction & Evaluation**: After training, the model predicts future prices, and its performance is evaluated using Mean Squared Error. * 📉 **Visualization**: `matplotlib` helps visualize the model's predictions against actual prices, providing clear insights into its accuracy. You can easily customize the stock ticker, date range, and the number of lag features (`N`) to experiment with different scenarios! This project serves as an excellent starting point for anyone interested in quantitative finance, algorithmic trading, or time series forecasting. While a basic model, it demonstrates the fundamental principles of using past data to inform future predictions, a concept transferable to various domains beyond finance. It highlights the power of simple yet effective ML techniques. #MachineLearning #StockPrediction #Python #DataScience #LinearRegression #TimeSeries #QuantitativeFinance #OpenSource

🚀 Today’s Learning & Projects – From Regression to Clustering! I spent the day diving deep into Python for Data Science & Machine Learning, and here’s what I accomplished: 1️⃣ Linear Regression Built a model to predict stock index prices using interest rates and unemployment rates. Calculated predictions and visualized actual vs predicted values with matplotlib. 2️⃣ K-Means Clustering (Synthetic & Real Data) Created complex synthetic datasets to test K-Means. Applied K-Means on both synthetic and real datasets like CC GENERAL.csv. Learned why scaling features is important for K-Means accuracy. Added cluster labels, visualized clusters, and explored cluster centers. 3️⃣ Hierarchical (Agglomerative) Clustering Used dendrograms to understand natural groupings in the Customer.csv dataset. Simplified the workflow using both AgglomerativeClustering from sklearn and fcluster from scipy. Assigned cluster labels to the dataset and visualized them. 💡 Key Takeaways: Feature scaling is crucial for clustering algorithms. Visualizations (scatter plots, dendrograms) make model results intuitive. K-Means and Hierarchical Clustering complement each other in exploratory analysis. Feeling more confident in data preprocessing, regression, clustering, and visualization! 🐍📊 #Python #MachineLearning #DataScience #KMeans #HierarchicalClustering #LinearRegression #LearningJourney