Khushi Sanwal’s Post

🌟🌟TASK 3: Uncovering Business Insights with Exploratory Data Analysis on 'SampleSuperstore'🌟🌟 As part of my internship at The Sparks Foundation, I conducted an in-depth Exploratory Data Analysis (EDA) on the 'SampleSuperstore' dataset to identify areas for improving profitability. Join me as I dive into the data to uncover business problems and provide actionable insights for boosting performance. This project demonstrates my ability to use R for data analysis and business strategy. YouTube Link : https://lnkd.in/gkyZp8EG GitHub Link : https://lnkd.in/gxMbaVqJ #GRIPMAY24 #TheSparksFoundation #TSF #GRIP #Internship #InternshipProject #DataScience #MachineLearning #DecisionTree #Classifier #DataVisualization #PredictiveModeling #GRIPMAY24 #TSF #GRIP #Internship #Python #RProgramming

TASK 4: Prediction using Decision Tree Algorithm. During my another task at The Sparks Foundation, I worked on Building And Visualising a Decision Tree Classifier .I developed a Decision Tree classifier using R. This model predicts classes for new data inputs with high accuracy. The project showcases my expertise in machine learning and data visualization. Watch the project in action: YouTube Link: https://lnkd.in/gcdn_qen Explore the code on GitHub: GitHub Link: https://lnkd.in/gjsdc7Ag #GRIPJULY24 #TheSparksFoundation #TSF #GRIP #Internship #DataScience #MachineLearning #DecisionTree #Classifier #DataVisualization #PredictiveModeling

🌟🌟TASK 2 : Determining Optimal Clusters in the Iris Dataset🌟🌟 For the 2nd Task as an intern in The Sparks Foundation , I worked on the Iris dataset to predict the optimum number of clusters using clustering techniques.I worked on R Programming Language to identify and visually represent these clusters. YouTube Link : https://lnkd.in/ggRQwwcu GitHub Link : https://lnkd.in/gdWq7p3Z #GRIPJULY24 #thesparksfoundation #tsf #grip #internship #InternshipProject #DataScience #Clustering #IrisDataset #DataVisualization Steps to Apply for The Sparks Foundation : https://lnkd.in/gbam9xmc

TASK 1 : Predicting Student Scores Using Simple Linear Regression As part of my internship at The Sparks Foundation, I worked on a data science task: predicting a student's percentage based on their study hours using simple linear regression. I used R Programming Language to predict the score for a student studying 9.25 hours per day. YouTube Link : https://lnkd.in/gwbQTd5F GitHub Link : https://lnkd.in/g4dqnC6r Website (The Sparks Foundation)- https://lnkd.in/gbam9xmc #GRIPJULY24 #thesparksfoundation #tsf #grip #internship #InternshipProject #DataScience #LinearRegression #StudentPerformance #PredictiveModeling

🌟🌟 TASK 3: Exploring Business Insights with Data Analysis on 'SampleSuperstore' 🌟🌟 During my internship at The Sparks Foundation I analyzed the 'SampleSuperstore' dataset to find ways to improve profitability. I used R to dig deep into the data, identifying business problems and suggesting actions to boost performance. This project showcases my skills in data analysis and strategic thinking. YouTube Link: https://lnkd.in/gB7cU8yr GitHub Link: https://lnkd.in/gzG3_j4t #GRIPJULY24 #TheSparksFoundation #DataScience #MachineLearning #DataVisualization #Internship #Python #RProgramming

🌟🌟TASK 2 : Determining Optimal Clusters in the Iris Dataset🌟🌟 For my internship at The Sparks Foundation , I explored the Iris dataset to predict the optimum number of clusters using clustering techniques. Watch my analysis to see how I applied R to identify and visually represent these clusters. This project showcases my skills in data science and machine learning, from data preprocessing to insightful visualizations. YouTube Link : https://lnkd.in/g2TmWFAd GitHub Link : https://lnkd.in/gsKQqEUm #GRIPMAY24 #thesparksfoundation #tsf #grip #internship #InternshipProject #DataScience #MachineLearning #Clustering #Python #IrisDataset #DataVisualization

Hello everyone! 🤗 It's been a while since I shared an update on LinkedIn. 🙈 🙆♂️ Recently, my days have been a blend of navigating the dynamic world of corporate life during my internship as a digital navigator – bridging the gap between business and technical teams – and immersing myself in upskilling activities. The challenge of juggling tasks, mastering office etiquettes, and delving into the intricacies of both realms became my daily routine. 🚴♀️ Amidst the hustle, a burning desire to elevate my skills ignited a parallel journey. The nights transformed into a canvas for self-improvement. Slowly but steadily, the pieces of the puzzle started falling into place. Real-world challenges at my internship provided the canvas for hands-on experience, while Python practice sessions seamlessly transformed abstract concepts into practical skills. 💡 Let's not forget, the journey to mastery is a marathon, not a sprint. Each task, whether in the workplace or in honing skills, contributes to the grand tapestry of success. The fusion of internship experience and my pursuit of Python for Professional Data Science is creating a symphony of professional achievement. 🚀 To fellow interns aspiring for greatness and Python enthusiasts weaving dreams through code – embrace the challenges, relish the struggles, for in the dance of difficulty lies the rhythm of success. 🌟 #InternshipJourney #PythonMastery #ProfessionalGrowth #DataScienceDreams 🐍💼

🌟 DataTalent Participant Project Highlights 🌟 💡 Our DataTalent program offers Canadian employers the opportunity to connect with outstanding data professionals through fully subsidized internships and projects. DataTalent Program is powered by Upskill Canada and the Government of Canada. Palette Skills 👥 Highlighting: Lilian Mandirasa's Project! ➡️ Project Description: In this project Lilian explores the relationship between age groups and the propensity to seek help for mental health concerns among professionals working in tech companies. The following information provides tech companies with actionable insights to enhance mental health support initiatives tailored to specific age groups https://lnkd.in/g9k48ekK

Last week, was the third week of my data science track internship at CUNY Tech Prep. During that week, our instructor Chris and T.A. Kevin Li had taught /explained to us the important concept of Data Visualization in respect to data science. Some of these concepts were, but not limited to: making sure that your data visualization can be easily interpreted right away, making sure that you don't clutter your data visualization with unnecessary visuals/coloring, making sure that the range for your data visualization starts from 0, and most importantly AVOID USING PIE CHARTS (jokingly, but also maybe not? 😅)! On a more serious note, in the real world, as a data scientist you will most likely be presenting data visualizations to "stakeholders" and if you are un-able to convey the message you want carefully, this can impact your/ your employer's performance. Thus, data visualization methodologies should not be taken with a grain of salt, as it is an important part, of one's data science toolkit!

I am happy to say that I have finished task 2 of my Data Science internship at Prodigy InfoTech. Task 2: Perform data cleaning and exploratory data analysis (EDA) on your preferred dataset, such as Kaggle's Titanic dataset. Examine the relationship between variables to identify patterns and trends in the data. Dive into the analysis: https://lnkd.in/d_GJj35W #ProdigyInfoTech #datascience #internship #EDA #datacleaning