Shashwat Soni’s Post

🚀 Just Completed My End-to-End Machine Learning Project: Predictive Maintenance System I’m excited to share my latest project where I built a complete Machine Learning system for Predictive Maintenance using XGBoost and deployed it using Flask API. 🔧 Project Highlights: • Data preprocessing & feature engineering • Trained XGBoost classification model • Model evaluation and optimization • Saved model using Pickle (.pkl) • Built Flask API for real-time predictions • REST API tested using JSON input 🧠 Tech Stack: Python | Pandas | NumPy | Scikit-learn | XGBoost | Flask | Jupyter Notebook 📌 Problem Statement: Predict whether a machine will fail based on sensor and operational data to reduce downtime and improve industrial efficiency. 💡 What I Learned: • End-to-end ML pipeline development • Model deployment using Flask • Real-world ML application design • API development and testing 📈 This project helped me understand how Machine Learning moves from notebooks to real-world deployment. #MachineLearning #DataScience #XGBoost #Flask #Python #PredictiveMaintenance #AI #MLOps #Projects https://lnkd.in/gnJu_XH5

🚀 Excited to share my first Data Science project! As part of my learning journey in Data Science, I have developed a Student Performance Prediction Dashboard that uses machine learning to analyze different factors influencing academic performance. The goal of this project is to demonstrate how data-driven insights can help understand student behavior and predict performance based on daily habits such as study hours, attendance, and social media usage. 📊 Project Overview This interactive dashboard allows users to input various student-related parameters and receive predictions about potential academic performance. Along with predictions, the system also provides personalized recommendations to help improve study habits and productivity. Through this project, I implemented the complete workflow of a data science application — starting from data preprocessing, feature preparation, model training, prediction generation, and finally building an interactive web dashboard. 🛠 Technologies and Tools Used • Python • Streamlit (for building the interactive web dashboard) • Pandas & NumPy (for data processing) • Scikit-learn (for machine learning model development) • Plotly (for interactive data visualizations) This project helped me gain hands-on experience with machine learning deployment and dashboard development, and it strengthened my understanding of how predictive models can be integrated into real-world applications. I’m continuously working on improving my skills in Data Science and Machine Learning and look forward to building more impactful projects. #DataScience #MachineLearning #Python #Streamlit #DataAnalytics #LearningJourney live dashboard:https://lnkd.in/gksA9AZf

From Jupyter Notebook to a Live AI Web App: My Stroke Risk Predictor is Live. I’m excited to share my latest project, a Stroke Risk Prediction App built using Python and Machine Learning. As a Data Scientist, I wanted to move beyond static charts and build a tool that provides real-time value. This project involved the full data lifecycle: ▪️Exploratory Data Analysis: Uncovered key risk drivers like Age and Glucose levels using a dataset of 5k+ patients. ▪️Modeling: Trained an XGBoost classifier, achieving high sensitivity for risk detection. ▪️Engineering: Implemented data scaling and label encoding to ensure the model handles real-world user inputs. ▪️Deployment: Built the UI with Streamlit and deployed it to the cloud via GitHub. It was a great challenge debugging environment dependencies and managing the deployment pipeline, but seeing the model provide instant probabilities is incredibly rewarding. Check out the live app here:https://lnkd.in/eCWKr2h9 View the code on GitHub:https://lnkd.in/e7-KAzQG #DataScience #MachineLearning #Streamlit #HealthTech #Python

Title: Customer Churn Prediction System - End-to-End ML App Description: I Developed an end-to-end machine learning application to predict customer churn risk using historical data. Key features: 1) Built a predictive model using XGBoost and Scikit-Learn. 2) Created an interactive web dashboard with Streamlit for real-time predictions. 3) Integrated dynamic data visualizations using Plotly. 4) Deployed the application on Streamlit Cloud for live access. Skills Machine Learning, Python (Programming Language), Streamlit, Data Visualization, XGBoost "Project supervised/mentored by: Faiz Ahmad, PhD". Github link: https://lnkd.in/dr-BdcYv Streamlit App:https: //rabiahafeez.streamlit.app/

Data science learning Update - Continuing my hands-on journey in Machine Learning with Scikit-learn 🚀 Recently worked through and implemented core steps of an end-to-end ML workflow using the California Housing dataset, including: ✅ Data Analysis (EDA) ✅ Creating a Stratified Test Set ✅ Feature Scaling ✅ Handling Categorical Data ✅ Further Data Preprocessing ✅ Building Pipelines with Scikit-learn ✅ Using ColumnTransformer for consolidated preprocessing ✅ Training ML algorithms on preprocessed data ✅ Model persistence and inference with Joblib This helped me understand not just model training, but the full preprocessing pipeline that happens before a model learns from data. One key takeaway: building a reliable ML solution is as much about data preparation and pipelines as it is about the algorithm itself. I’ve pushed my notebooks and progress to GitHub here: 🔗 https://lnkd.in/gwJzik-S Learning, practicing, and building one step at a time. #MachineLearning #ScikitLearn #Python #DataScience #EDA #FeatureEngineering #LearningInPublic #GitHub #StudentDeveloper

🚀 𝐏𝐫𝐨𝐣𝐞𝐜𝐭: 𝐈𝐧𝐭𝐞𝐫𝐚𝐜𝐭𝐢𝐯𝐞 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐖𝐞𝐛 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧 I’m excited to share my new Machine Learning Classifier web application, built using 𝐏𝐲𝐭𝐡𝐨𝐧 and 𝐅𝐥𝐚𝐬𝐤 framework to create a seamless, interactive user experience. As an engineer, I wanted to create a tool that doesn't just "run code" but visualizes the entire data science pipeline—from raw data to performance evaluation. ✨ 𝐊𝐞𝐲 𝐅𝐞𝐚𝐭𝐮𝐫𝐞𝐬: 𝐃𝐲𝐧𝐚𝐦𝐢𝐜 𝐃𝐚𝐭𝐚 𝐔𝐩𝐥𝐨𝐚𝐝: Users can upload any dataset for classification. 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐞𝐝 𝐏𝐫𝐞𝐩𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠: The backend handles data cleaning and preparation automatically. 𝐌𝐨𝐝𝐞𝐥 𝐒𝐞𝐥𝐞𝐜𝐭𝐢𝐨𝐧: Choose between various algorithms (including KNN, SVM, and Decision Trees) with built-in educational tooltips for each. 𝐈𝐧𝐭𝐞𝐫𝐚𝐜𝐭𝐢𝐯𝐞 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧𝐬: Real-time generation of graphs (Scatter, Bar, and Line) to understand data distribution before training and evaluate results afterward. 𝐅𝐮𝐥𝐥 𝐏𝐢𝐩𝐞𝐥𝐢𝐧𝐞 𝐓𝐫𝐚𝐧𝐬𝐩𝐚𝐫𝐞𝐧𝐜𝐲: The app displays each phase—Preprocessing, Training, and Evaluation—clearly. 💻 𝐓𝐞𝐜𝐡 𝐒𝐭𝐚𝐜𝐤: 𝐁𝐚𝐜𝐤𝐞𝐧𝐝: Python, Flask 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞: Pandas, Scikit-Learn 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Matplotlib, Seaborn This project gave me great hands-on experience in testing models and helped me understand the practical steps needed to make a machine learning model work. Check out the video below to see it in action! 📽️ #MachineLearning #Python #Flask #AI #Coding #ElectricalEngineering #DataVisualization

🚀 My Data Science Learning Journey: NumPy & Pandas Over the past few days, I’ve been diving deep into the foundations of Data Analysis using Python, focusing on NumPy and Pandas—two of the most powerful libraries every data enthusiast should master. Here’s a quick snapshot of what I explored 👇 🔹 📌 NumPy (From Basics to Advanced) Array creation & comparison with Python lists Understanding array properties: shape, size, dimensions, data types Mathematical & aggregation operations Indexing, slicing, and boolean masking Reshaping & manipulating arrays Advanced operations: append, concatenate, stack, split Broadcasting & vectorization for optimized performance Handling missing values with np.isnan, np.nan_to_num 🔹 📊 Pandas Part 1 – Data Handling Essentials Reading data from CSV, Excel, JSON files Saving/exporting data into different formats Exploring datasets using .head(), .tail(), .info(), .describe() Understanding dataset structure (shape, columns) Filtering rows & selecting columns efficiently 🔹 📈 Pandas Part 2 – Advanced Data Analysis DataFrame modifications (add, update, delete columns) Handling missing data using isnull(), dropna(), fillna(), interpolate() Sorting and aggregating data GroupBy operations for insights Merging, joining, and concatenating datasets 💡 Key Takeaway: Learning these libraries helped me understand how raw data is transformed into meaningful insights—efficiently and at scale. 📂 I’ve also documented my entire learning through hands-on notebooks covering concepts + code implementations. 🔥 What’s Next? Moving forward, I’m planning to explore: ➡️ Data Visualization (Matplotlib & Seaborn) ➡️ Exploratory Data Analysis (EDA) ➡️ Machine Learning basics #DataScience #Python #NumPy #Pandas #LearningJourney #MachineLearning #DataAnalytics #Students #Tech

🚀 Excited to share my Machine Learning Project: House Price Prediction App I recently built a web application that predicts house prices based on key features like area, BHK, and city. This project helped me understand the complete end-to-end machine learning workflow. 🔍 Problem Statement The goal was to predict house prices using important factors such as property size, number of bedrooms, and location. This can help users estimate property value efficiently. 📊 Dataset The dataset includes: • Size_in_SqFt (area) • BHK (number of bedrooms) • City (location) • Price_in_Lakhs (target variable) 🧠 Approach 1️⃣ Data Preprocessing • Removed missing values • Converted categorical data (city) into numerical format using one-hot encoding 2️⃣ Feature Engineering • Created city-based features to improve prediction accuracy 3️⃣ Model Building • Used Random Forest Regressor for better performance • Split data into training and testing sets 4️⃣ Feature Scaling • Applied StandardScaler to normalize data 5️⃣ Model Training • Trained the model on processed data to learn relationships between inputs and price 💾 Model Deployment • Built an interactive web app using Streamlit • Users can input area, BHK, and city to get predictions 📊 Output • Predicted house price (in Lakhs) • Price per square foot for better insights 🛠 Tech Stack Python | Pandas | Scikit-learn | Streamlit 💡 Key Learning This project helped me understand data preprocessing, feature engineering, model training, and deploying machine learning models in a real-world application. 🔗 GitHub: https://lnkd.in/gN9CZb8P I would love to hear your feedback and suggestions! #MachineLearning #Python #DataScience #Streamlit #Projects #Learning

Polars or pandas for dataframes? I recently asked one of the developers, and this is what I found: 🖥️From a technical perspective, there is little reason to remain with pandas: 👉Polars is significantly ahead. It has addressed many of the long-standing issues pandas has struggled with, while offering a clearer API and much faster performance. 👉Pandas is unlikely to change dramatically, while polars is evolving quickly. That means the tech gap between the performance of the 2 libraries will continue to widen. In practice: 👉Few people move from polars to pandas, while many users are transitioning from pandas to Polars. 👉Still, pandas is huge compared to Polars. In fact, if you check the summary made by MLcontests about the data science competitions in 2025, you’ll notice that Pandas is still the go-to library for dataframe manipulation, used in 61 competitions vs 5 using polars. 💡Pandas popularity will not change overnight, which means that pandas will likely remain widely used and, for a long time, more popular overall. So, which library should you use? In short: 👉Are you new to Python and dataframes ⇒ then learn polars 👉Working with legacy code? You are not alone and pandas is here to stay for many years, so your learnings will not be wasted Which library do you use? Let me know in the comments 👇 #machinelearning #ml #dataframes #polars #pandas #mlonline #mlcourse #trainindata #datascience #datascientist #dataengineer #dataengineering #mleducation #mlcareer #ai #python

I’ve started revising Machine Learning fundamentals from scratch and documenting my learning step by step. Instead of just using libraries, I’m focusing on understanding the core concepts behind how things work. I’m starting with Statistics, because it forms the foundation of Machine Learning. Topics I’ll be covering in this phase: What is data and types of data Descriptive statistics (mean, variance, standard deviation) Data distribution Correlation Probability basics My approach: Understand the concept in simple terms Implement it using Python (from scratch) Visualise wherever possible Organise everything clearly on GitHub I’ll be sharing my progress regularly as I move from statistics → feature engineering → machine learning algorithms. GitHub repository: [https://lnkd.in/gyvJrq-Y] If you’re also learning ML, feel free to follow along.