Reshma Mani’s Post

Why data visualization is so important? There’s a famous statistical example called Anscombe’s quartet that perfectly illustrates this. It consists of four datasets and their descriptive statistics are the same: They have the same mean, variance, correlation and even regression line. But this “average behavior” tells very little about what’s actually going on with the data. When the data is plotted, we see a completely different pattern: • One shows a clear linear relationship • Another hides a curve • One is driven by a single outlier • Another looks random except for one influential point This is why visualization matters: 👉 It exposes patterns that summary metrics hide 👉 It reveals outliers that can mislead your models 👉 It helps avoid false conclusions 👉 It turns abstract numbers into intuitive insight And the best part? It’s incredibly easy to get started. With Python, just a few lines using libraries like matplotlib or seaborn can completely change how you understand your data. A simple scatter plot can reveal what pages of statistics cannot. Before you trust the model, plot the data. #DataScience #DataVisualization #Python #Analytics #MachineLearning #DataAnalytics #BigData #DataDriven #Statistics #AI #ArtificialIntelligence #DataLiteracy #BusinessIntelligence #DataStorytelling #Insight #PredictiveModeling #DeepLearning #ExploratoryDataAnalysis #STEM #Tech #Innovation

What if I told you… Machine Learning can be seen, not just coded? 👀 I built a 3D KNN clustering visualization using real cricket player data — and the results are fascinating. Each dot you see represents a player, mapped in 3D space using: 🏏 innings played 🏃 runs scored 💯 centuries made But here’s where it gets interesting… The algorithm doesn’t “know” the players — it only knows distance. And yet… it starts forming meaningful groups ✨ 🔄 As the graph rotates, you can literally watch how similarity drives clustering in space. No magic. Just mathematics + patterns + data 💡 What this taught me: Machine Learning becomes truly powerful when you visualize what’s happening behind the scenes. 🛠️ Tools Used: Python • Plotly • Pandas • KNN • Data Visualization #MachineLearning #DataScience #KNN #Python #AI #DataVisualization #Analytics #MLProjects

Revisiting Multiple Linear Regression – My ML Learning Journey As part of my ongoing machine learning journey, I revisited Multiple Linear Regression using a car dataset to strengthen my fundamentals and deepen my understanding. 🔍 What I focused on this time: • Practicing exploratory data analysis and understanding feature relationships • Visualizing how variables like HP, VOL, SP, and WT impact MPG • Building multiple models with different feature combinations • Evaluating performance using RMSE and R² score 📊 What I observed: As I added more relevant features, the model performance improved — giving a clearer picture of how multiple factors influence fuel efficiency. 💡 Why this revision mattered: Reworking the same concept helped me move beyond just “knowing” regression to actually understanding how feature selection impacts model performance. 🛠️ Tech Stack: Python | Pandas | NumPy | Matplotlib | Scikit-learn Still learning, still improving — one concept at a time. #MachineLearning #DataScience #Python #Regression #LearningJourney #DataAnalytics

🚀 Day 36/70 – Random Variables Today I learned about Random Variables in Statistics 📊 A random variable represents the numerical outcome of a random process. 📌 Types of Random Variables 1️⃣ Discrete Random Variable Takes specific values Example: Number of heads in coin toss 2️⃣ Continuous Random Variable Takes any value within a range Example: Height, weight, temperature 📌 Python Example import numpy as np # Discrete random values data = np.random.randint(1, 10, 5) print("Discrete:", data) # Continuous random values data2 = np.random.random(5) print("Continuous:", data2) 📊 Why It’s Important ✔ Forms the base of probability theory ✔ Used in statistical modeling ✔ Helps in predicting outcomes ✔ Important for machine learning Today’s Learning: Random variables help convert real-world uncertainty into numbers 🔥 Day 36 completed 💪 Advancing deeper into statistics! #Day36 #Statistics #Probability #DataAnalytics #Python #LearningInPublic #FutureDataAnalyst #70DaysChallenge

🚀 Day 37/70 – Probability Distributions Today I learned about Probability Distributions in Statistics 📊 Probability distributions describe how values of a random variable are distributed. ⸻ 📌 Types of Probability Distributions 1️⃣ Discrete Distribution • Takes specific values • Example: Number of heads in coin toss 2️⃣ Continuous Distribution • Takes any value in a range • Example: Height, weight ⸻ 📌 Common Distributions ✔ Normal Distribution (Bell-shaped) ✔ Binomial Distribution (Success/Failure) ✔ Uniform Distribution (Equal probability) ⸻ 📌 Python Example import numpy as np # Generate normal distribution data data = np.random.normal(0, 1, 1000) print(data[:10]) ⸻ 📊 Why It’s Important ✔ Helps understand data behavior ✔ Used in statistical modeling ✔ Important for machine learning ✔ Helps in prediction and analysis ⸻ Today’s Learning: Probability distributions help model real-world uncertainty 🔥 Day 37 completed 💪 Deep diving into statistics now! #Day37 #Statistics #Probability #DataAnalytics #Python #LearningInPublic #FutureDataAnalyst #70DaysChallenge

Yesterday I decided to build a Multiple Linear Regression model simple, right? 😄 Well, not exactly. I ran into one of the weirdest issues I’ve ever seen in a dataset. I have my own data preprocessing template tested many times, reliable, and saves me a lot of time. So I trusted it 100%. But when I applied it and selected the independent and dependent variables I got results that made ZERO sense. At first, I thought: “Okay maybe I messed up something small.” Then I tried again. And again. And again. Same weird output. At this point, I started questioning everything even my own template 😅 Before giving up, I tried one last thing: Instead of selecting columns by index, I used column names. And suddenly everything worked perfectly 🤯 So I went back to investigate further And here’s the surprise: The column indices I was using didn’t match what actually existed in the dataset! 👉 Turns out there were hidden columns / unexpected structure issues messing with the indexing. Lesson learned: Never trust indices blindly Always double check your dataset structure And sometimes column names will save your life 😄 Debugging data > building models sometimes. Has anyone faced something like this before? #DataScience #MachineLearning #DataPreprocessing #Python #DataAnalytics #AI #Debugging

Just finished a Machine Learning project predicting loan approval using a real-world loan dataset. Goal: Build a model that predicts whether a loan application should be approved based on applicant financial and personal data. What I worked on: 1-Data Preparation Handled missing values & outliers Encoded categorical variables Scaled numerical features Built a clean pipeline ready for modeling 2-Modeling & Comparison Trained and compared multiple classification models: Logistic Regression KNN Decision Tree Random Forest 3-Evaluation Models were evaluated using: Accuracy, Precision, Recall, and F1-score to ensure real performance and avoid misleading results. Why Random Forest performed best Random Forest achieved the highest accuracy because: It combines multiple decision trees → reduces overfitting Captures non-linear relationships in financial data better than linear models Handles feature interactions automatically More robust to noise and outliers than a single Decision Tree Key Takeaway Choosing the right model isn’t about complexity — it’s about how well the model matches the nature of the data. Tools: Python | Pandas | NumPy | Scikit-learn | Matplotlib | Seaborn #MachineLearning #DataScience #AI #Python #Classification #RandomForest

𝐉𝐮𝐬𝐭 𝐜𝐨𝐦𝐩𝐥𝐞𝐭𝐞𝐝 𝐨𝐮𝐫 𝐃𝐫𝐲 𝐁𝐞𝐚𝐧𝐬 𝐂𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐌𝐋 𝐏𝐫𝐨𝐣𝐞𝐜𝐭 🌱 Collaborated with Taimoor Tahir Satti 𝐃𝐚𝐭𝐚𝐬𝐞𝐭: 13,000+ records | 16 Features | 7 classes 𝐌𝐨𝐝𝐞𝐥 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞: Achieved 93%+ Accuracy, with Precision, Recall, and F1-Score all above 90%, ensuring balanced and reliable predictions across classes. 𝐖𝐡𝐚𝐭 𝐰𝐞 𝐝𝐢𝐝 𝐢𝐧 𝐭𝐡𝐢𝐬 𝐩𝐫𝐨𝐣𝐞𝐜𝐭: ● Exploratory Data Analysis (EDA) ● Outlier Detection & Handling ● SMOTE (handling class imbalance) ● Cross Validation ● Hyperparameter Tuning ● Trained & compared models (SVM, Random Forest, XGBoost) 𝐓𝐞𝐜𝐡 𝐒𝐭𝐚𝐜𝐤: Python, NumPy, Pandas, Matplotlib, Seaborn, Plotly, ydata-profiling, Scikit-learn, XGBoost, Streamlit 𝐏𝐫𝐨𝐣𝐞𝐜𝐭 𝐋𝐢𝐧𝐤𝐬: 🔗 Dataset: https://lnkd.in/dUPSMx_c 🔗 GitHub Repo: https://lnkd.in/dFSJq6zT 🔗 Live App: https://lnkd.in/d-E7kUjX We’ve been learning Machine Learning for around 1–1.5 months, mainly focusing on classical ML, and now moving towards Deep Learning and advanced topics. This is one of our first complete end-to-end + deployed ML projects, and a big step in our journey. Open to feedback and suggestions. #MachineLearning #DataScience #Python #AI #MLProjects #XGBoost #ScikitLearn #Streamlit #EDA #LearningJourney #F1Score #DataAnalytics #DeepLearning

Just built my Personal AI Data Analyst! An interactive dashboard where you can upload any dataset (CSV/Excel/JSON) and get instant AI-powered insights — no coding required! 🔍 What it does: Auto-suggests relevant analyses based on your data Generates histograms, scatter plots & correlation heatmaps Detects anomalies using z-score Supports custom prompts via local LLM (Ollama) 🛠️ Built with: Python • Streamlit • Pandas • Matplotlib • NumPy This project taught me how to build end-to-end AI-powered data tools from scratch — from file parsing to code execution to LLM integration. 🔗 GitHub: https://lnkd.in/g376qyyK #Python #DataScience #MachineLearning #Streamlit #AI #DataAnalysis #OpenSource #BuildInPublic

𝐒𝐭𝐨𝐩 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐌𝐨𝐝𝐞𝐥𝐬 𝐔𝐧𝐭𝐢𝐥 𝐘𝐨𝐮 𝐃𝐨 𝐓𝐡𝐢𝐬 𝐅𝐢𝐫𝐬𝐭. Your ML results don’t start with algorithms - they start with clean, model-ready data. 🚀 Here’s a simple 𝗗𝗮𝘁𝗮 𝗣𝗿𝗲-𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 checklist you can follow every time 👇 𝟭) 𝗜𝗺𝗽𝗼𝗿𝘁 𝘁𝗵𝗲 𝗟𝗶𝗯𝗿𝗮𝗿𝗶𝗲𝘀 📚 Bring in the basics: ✅ NumPy | ✅ Pandas | ✅ (Optional) Matplotlib/Seaborn | ✅ Scikit-learn 𝟮) 𝗜𝗺𝗽𝗼𝗿𝘁 𝘁𝗵𝗲 𝗗𝗮𝘁𝗮𝘀𝗲𝘁 🗂️ Load your data and do quick checks: 🔍 shape, column types, sample rows, basic stats 𝟯) 𝗛𝗮𝗻𝗱𝗹𝗲 𝗠𝗶𝘀𝘀𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 🧩 (𝗜𝗺𝗽𝘂𝘁𝗲𝗿) Missing values can silently hurt accuracy. Fix them with: 📌 Mean/Median (numerical) 📌 Mode (categorical) 𝟰) 𝗘𝗻𝗰𝗼𝗱𝗲 𝗖𝗮𝘁𝗲𝗴𝗼𝗿𝗶𝗰𝗮𝗹 𝗗𝗮𝘁𝗮 🔤➡️🔢 Models need numbers, not text. ✅ 𝗜𝗻𝗱𝗲𝗽𝗲𝗻𝗱𝗲𝗻𝘁 𝗩𝗮𝗿𝗶𝗮𝗯𝗹𝗲𝘀 (𝗫): 𝗢𝗻𝗲-𝗛𝗼𝘁 𝗘𝗻𝗰𝗼𝗱𝗶𝗻𝗴 🧱 Example: City → City_NY, City_LA, City_SF ✅ 𝗗𝗲𝗽𝗲𝗻𝗱𝗲𝗻𝘁 𝗩𝗮𝗿𝗶𝗮𝗯𝗹𝗲 (𝘆): 𝗟𝗮𝗯𝗲𝗹 𝗘𝗻𝗰𝗼𝗱𝗶𝗻𝗴 🎯 Example: Yes/No → 1/0 𝟱) 𝗦𝗽𝗹𝗶𝘁 𝗧𝗿𝗮𝗶𝗻 𝘃𝘀 𝗧𝗲𝘀𝘁 ✂️ Common split: 𝟴𝟬/𝟮𝟬 or 𝟳𝟬/𝟯𝟬 🎯 Train = learn patterns | Test = validate performance 𝟲) 𝗙𝗲𝗮𝘁𝘂𝗿𝗲 𝗦𝗰𝗮𝗹𝗶𝗻𝗴 ⚖️ Helps models learn fairly when features have different ranges. 📍 Standardization (Z-score) 📍 Normalization (Min-Max) 🔥 Especially important for: 𝗞𝗡𝗡, 𝗦𝗩𝗠, 𝗞-𝗠𝗲𝗮𝗻𝘀, 𝗟𝗼𝗴𝗶𝘀𝘁𝗶𝗰 𝗥𝗲𝗴𝗿𝗲𝘀𝘀𝗶𝗼𝗻 #MachineLearning #DataScience #FeatureEngineering #DataPreprocessing #Python