Pratiksha Saheb’s Post

Exciting News! 🎉 Thrilled to share that I've successfully completed two data science projects as part of the CodeClause Internship program! 🚀 Predicting House Prices 🏠📈 As an Entry-Level project, I built a simple linear regression model to predict house prices based on features like the number of bedrooms and square footage. Leveraging Python and libraries like Pandas and Scikit-learn, I explored a dataset of house prices, split it into training and testing sets, and developed regression models to make accurate predictions. https://lnkd.in/dpiHC9q8 Customer Lifetime Value Prediction 💼💰 For the Intermediate-Level project, I applied regression techniques to estimate the future value that a customer will bring to the business. By analyzing customer interactions and segmenting customers based on Recency, Frequency, and Revenue (RFM) scores, I developed insights into customer behavior and predicted their lifetime value. https://lnkd.in/dMggdR8E These projects have been a fantastic learning experience, allowing me to enhance my skills in regression modeling and predictive analytics. A big thank you to CodeClause Internship for providing such valuable opportunities! 🙌 #DataScience #MachineLearning #PredictiveAnalytics #CodeClauseInternship #DataScienceIntern #RegressionModeling #Python

🎉 I am thrilled to announce the successful completion of my internship with CodeClause! 🌟 During my tenure as a Data Science Intern at CodeClause, I had the opportunity to work on several industry-level projects that significantly enhanced my skills and knowledge. Here are some highlights of my experience: Hands-On Projects: I contributed to and successfully completed multiple projects, applying advanced data science techniques to solve real-world problems. Technological Proficiency: I gained in-depth knowledge of cutting-edge technologies, including Machine Learning (ML) and various Python libraries essential for data science. Skill Development: This internship honed my analytical and problem-solving skills, preparing me for future challenges in the data science field. Timely Execution: I managed to deliver all projects within the stipulated timelines, demonstrating my ability to work efficiently under pressure. This experience has been invaluable in my professional development, and I am excited to apply what I have learned to future endeavors. #internship #datascience #machinelearning #python #professionaldevelopment #CodeClause #grateful

Hello LinkedIn fam! 🌟 Absolutely thrilled to announce the successful completion of my latest project! 🚀 During my internship at CodeClause, I took on the challenge of building a simple linear regression model to predict house prices. 🏡 Utilizing cutting-edge technologies like Python, Pandas, and Scikit-learn, I dove into a rich dataset of house prices. The goal was to leverage features such as the number of bedrooms and square footage for accurate predictions. 📊✨ I split the dataset into training and testing sets, employing advanced techniques from Scikit-learn. This project not only allowed me to showcase my skills in data manipulation and modeling but also provided valuable insights of regression modeling and prediction. 🧠🌐 The journey was incredibly rewarding, and I am grateful for the opportunity to apply my knowledge to real-world scenarios. Excited about the possibilities that lie ahead in the dynamic field of data science! 🌟💻 GitHub Repository: https://lnkd.in/djCNc4iF #datascience #machinelearning #RegressionModeling #CodeClauseInternship #internship #LinkedInCommunity 🚀

Hi LinkedIn Family 🙋♂️ , I successfully completed #task3 of my Data Science Internship at CodeClause. Project Level: Golden Level Project Title: Heart Disease Risk Assessment Domain: Data Science Technologies Used: Pandas, Matplotlib, Seaborn, Scikit-Learn and Streamlit (For Web Application) #datascience #machinelearning #dataanalysis #internship #taskcompletion #codeclause #intern #datascienceintern #datascientist #prediction #webapp #streamlit #taskcomplete #heartdiseaseprediction

Hi LinkedIn Family 🙋♂️ , I successfully completed #task2 of my Data Science Internship at CodeClause. Project Level: Intermediate Level Project Title: Predicting Employee Attrition Domain: Data Science Technologies Used: Pandas, Matplotlib, Seaborn and Scikit-Learn #datascience #machinelearning #dataanalysis #internship #taskcompletion #codeclause #intern #datascienceintern #datascientist #prediction

Hello LinkedIn family! 🌟 I'm thrilled to share the successful completion of Task-1 - Customer Segmentation during my internship at CodeClause in the exciting field of data science! 🚀 In this project, I utilized cutting-edge technologies like NumPy, Pandas, and scikit-learn to dive into the world of customer segmentation. The primary goal was to classify customers based on various shopping criteria such as gender, amount spent, and previous purchases. The analysis of the data involved leveraging powerful techniques like the K-means algorithm and the elbow method. 🛍️💻 Curious minds can explore the details and nuances of my work on my GitHub repository: https://lnkd.in/gKe94j-y I'm immensely proud of the insights gained from this project, and I can't wait to apply these learnings to more exciting challenges in the world of data science. Let's connect, collaborate, and celebrate the power of data! 🤝🔗 #DataScience #CustomerSegmentation #CodeClauseInternship #Internship #LinkedInFamily

🚀 TASK completed 🚀 As part of my online internship with Tech Straver, I developed a House Price Prediction model using machine learning techniques. This project aims to predict house prices based on various features(price,area,location), providing valuable insights into the real estate market. This helps buyers, sellers, and real estate agents make informed decisions. In this video, I demonstrate the project execution, highlighting the data preprocessing, model training, and prediction process. A huge thank you to the Techstraver team for their continuous support and guidance. Check out the video to see the house price prediction model in action! #internship #Techstraver #HousePricePrediction #MachineLearning #DataScience #Python #task

🚀 Excited to share the culmination of my summer internship at HopingMinds! During my time here, I developed a Salary Prediction ML Model with a user-friendly interface using Streamlit. This project includes a feature to predict salaries based on various inputs and an explore page to visualize and interact with the data. 🔧 Technologies and tools used: Pandas for efficient data manipulation Matplotlib for clear and insightful data visualization Label Encoding for handling categorical data Decision Tree for constructing the prediction model Random Search and GridSearchCV for optimizing model performance through hyperparameter tuning This project has been a fantastic learning experience, allowing me to apply theoretical knowledge to practical problems. I'm proud of the results and the insights gained throughout the process. Check out the project on GitHub for more details and to see the code in action: GitHub Link : https://lnkd.in/gsFc7SR8 #MachineLearning #DataScience #AI #Internship #HopinMinds #Streamlit #SalaryPrediction #TechJourney #Python #DataVisualization

A super simple, super easy Movie #Recommendation System for my Growintern #Internship (#DataScience) Task 1! I used plot-keywords and genre based similarity, using #TFIDF models on a 1-million #movies #dataset from Kaggle! Next step? Building a UI for this baby! git-link: https://lnkd.in/dqQDnEAr theme: Rouge (https://lnkd.in/dVCDPXx8) (TASK 2 will be posted tomorrow!)

🚀 Data Science Internship at Prodigy InfoTech : Task 01 Completed! 🚀 I just wrapped up a data visualization task, and I couldn’t be more excited to share my progress! My latest project involved visualizing the gender distribution within populations across multiple countries and years, utilizing datasets from the World Bank. 📝 Here’s what I accomplished: 1. Data Wrangling: I worked with three separate datasets—total population, male population, and female population—and transformed them from wide format (columns for each year) to long format using `pd.melt()`. This restructuring allowed me to merge the data and analyze it more effectively.    2. Data Cleaning: Before jumping into visualization, I checked for any missing values and removed rows containing null data to ensure accuracy in analysis. 3. Calculations & Analysis: I calculated gender proportions for each year and country, enabling me to observe how gender distribution varies over time and across different regions. 4. Visualization: Finally, I created both bar charts and stacked bar charts in Python’s Matplotlib to illustrate the gender breakdown per country and over time. This was an excellent opportunity to enhance my data visualization skills and practice presenting complex data in a clear and impactful way. 💡 Key Takeaways: Data Manipulation, Data Quality, Effective Visualization Github link : https://lnkd.in/eyj6BqD2 #datascience #ProdigyInfoTech #internship