Educative’s Post

New Project: Simple Linear Regression Analysis Using Python In this project, I explore the relationship between CGPA and package prediction using Simple Linear Regression. The analysis demonstrates how we can predict outcomes based on data through core Python libraries such as Pandas, Seaborn, and Scikit-Learn. Project Highlights: - Visualized the data with scatter plots - Trained a linear regression model to predict packages based on CGPA Achieved a model accuracy of 77.31% on the test data - The full analysis is available in the PDF, and the code has been uploaded to GitHub. I'm open to discussing insights and potential applications. GitHub Repository Link - https://lnkd.in/dZ-vHeu4 #Python #MachineLearning #DataScience #LinearRegression #PredictiveModeling #CareerGrowth

#10dayspostlearningchallenge Day 2: Lists! Storing and Manipulating Data I explored Python lists, which are collections of items that can be of any data type, including strings, integers, and other lists. I learned how to: - Create lists using square brackets [] - Index and access individual elements - Slice lists to extract subsets - Append, insert and remove elements - Concatenate lists Lists are powerful data structures in Python, and I'm excited to use them for data manipulation and analysis. Tomorrow, I'll talk about functions and methods. #Python #DataScience #Lists #DataManipulation #CodeSnippets #LearningJournal

#day 35& 36 I learnt Introduction to Seaborn seaborn is a powerful Python library that makes it easy to create informative and attractive data visualizations creating scatter plot with list making count plot with list using pandas with seborn making count plot with dataframe Hue with scatter plot Hue with count plot excercise survey data how many young people surveyed report being scared of spiders? Survey participants were asked to agree or disagree with the statement "I am afraid of spiders". Responses vary from 1 to 5, where 1 is "Strongly disagree" and 5 is "Strongly agree" #datacamp #dataanalytic #ingressiveforgood #python #100daysoflearning

🌟Late Post: Data Preprocessing with Python Project 🌟 Excited to share a glimpse of my recent project where I delved into data preprocessing using Python! 📊🐍 In this project, I focused on: 1. Initial Data Exploration 2. Handling Missing Values 3. Encoding Categorical Data 4. Outlier Detection 🔗 Check out the full details and code on my GitHub: https://lnkd.in/gSeAnvNT This journey has enhanced my understanding of key data preprocessing techniques essential for any data analysis or machine learning project. Check out the detailed process and results in the attached presentation! 📈💡 #DataScience #MachineLearning #Python #DataPreprocessing #DataAnalysis #LatePost #LearningJourney

I've just begun delving into Python and, surprisingly, it becomes more comprehensible once you get the hang of it. Here's what I've managed to create so far: - Graphs: It's simpler to generate graphs with Pyplot by specifying the plot type, such as bar, pie, etc. - I/O: I can import files using Pandas from one location, manipulate the data, and export the results to the desired location. I am eager to apply these newly acquired skills to my ongoing learning projects, particularly in the field of data analysis. #Python #Programming #DataManipulation #CodingBeginner

𝗙𝗿𝗲𝗲 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝗳𝗶𝗰 𝗣𝘆𝘁𝗵𝗼𝗻 𝗧𝘂𝘁𝗼𝗿𝗶𝗮𝗹𝘀!🖥️📊 Scientific Python Lectures is a website that provides free tutorials for the scientific Python ecosystem. The updated 2024 version covers numerous libraries, including NumPy, Matplotlib, SciPy, SymPy, statsmodels, scikit-learn and more! The tutorials can be accessed as HTML web pages, or downloaded as a PDF document. This website is a valuable resource for beginners and experts alike, so I suggest checking it out. Visit the links below for more information, and make sure to follow me for regular data science content! 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝗳𝗶𝗰 𝗣𝘆𝘁𝗵𝗼𝗻 𝗟𝗲𝗰𝘁𝘂𝗿𝗲𝘀 𝘄𝗲𝗯𝘀𝗶𝘁𝗲: https://lnkd.in/ditEiRsr 𝗟𝗲𝗮𝗿𝗻 𝗠𝗟 𝗮𝗻𝗱 𝗧𝗦 𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗣𝘆𝗖𝗮𝗿𝗲𝘁📚: https://lnkd.in/dyByK4F #datascience #python #machinelearning #deeplearning #linkedin

🔍 Exploring Python Fundamentals: Operators, If-Else, and Iteration 🔄 I'm thrilled to share the second part of my data science journey resources, focusing on essential Python concepts: operators, if-else statements, and iteration. This module builds on the basics and is crucial for anyone diving deeper into programming and data analysis. In this notebook, you'll explore: Various types of operators and their usage Conditional logic with if-else statements Looping techniques with for and while loops Real-world examples to solidify understanding These concepts are the backbone of writing efficient and effective code, and I'm excited to help you strengthen these skills. Check out the notebook and let me know your thoughts! I'm eager to continue sharing more resources and insights as we progress. Let's keep learning and supporting each other in this journey! #Python #DataScience #MachineLearning #DataAnalysis #JupyterNotebooks #LearningJourney #CommunitySupport

🌟 Excited to share that I've completed the "Python Matplotlib" course offered by Great Learning Academy! 🎉 This course provided me with in-depth knowledge of Matplotlib, a powerful Python library used for creating dynamic and visually appealing data visualizations. Throughout the course, I learned how to: Create a variety of plots and charts, including line plots, bar charts, histograms, and scatter plots. Customize visualizations to make data insights more intuitive and impactful. Integrate Matplotlib with other Python libraries like NumPy and Pandas to enhance data analysis workflows. This experience has significantly boosted my ability to present data in a more compelling and informative way, making it easier to communicate insights effectively. #Python #Matplotlib #DataVisualization #Learning #GreatLearningAcademy

🌍 Hello everyone! I am sharing my Python project on the World Happiness Dataset. I brought together info from 2015, 2016, and 2017 to make one big dataset. I made sure the data was super clean by getting rid of any copies and making sure it was accurate. 💡 A critical aspect was handling missing values in both categorical and numerical features. 📉 For categorical variables, I strategically filled missing values with pertinent data, preserving the integrity of happiness metrics. Simultaneously, for numerical features, I utilized appropriate imputation techniques to address gaps and establish a resilient dataset for analysis. Github Profile: https://lnkd.in/enNcckRH Project Link: https://lnkd.in/eC22AgWw #DataPreprocessing #Python #DataAnalysis #DataScience #PandasLibrary

Today, let's unlock the potential of NumPy, Matplotlib, and Regex – the dynamic trio for conquering data challenges. NumPy: Crunch numbers effortlessly with powerful mathematical operations. Matplotlib: Turn data into stunning visuals with ease. Regex: Master the art of pattern recognition in text. Ready to transform problems into solutions? Join the Python adventure! Comment below with your favorite Python tool or share a challenge you've faced. Let's build a community of Python enthusiasts! atomcamp Python Coding #PythonMagic #DataScience #Programming #NumPy #Matplotlib #RegexMagic #DataAnalytics #CodingCommunity #LearningPython #AIProgramming #CodeLikeAPro