Vineela Priya Kalasani’s Post

📊 Leveling Up My Pandas Skills! 🐼 Excited to dive into some essential pandas practice questions on Kaggle! 🚀 Working through these exercises has helped me deepen my understanding of data manipulation, cleaning, and analysis – all while sharpening my Python skills. 🔍 Whether it's filtering rows, groupby operations, or handling missing data, there's always something new to learn with pandas. Highly recommend checking out Kaggle's resources for anyone looking to strengthen their data wrangling toolkit! #Pandas #DataScience #Kaggle #Python #Learning #DataAnalysis #AlmaBetter Here is the link of colab:

Excited to share my latest project! I've built a machine learning model to predict house prices using Python and scikit-learn. Here's a brief overview of the process: 1. **Data Preparation**: Loaded and split the dataset into training and testing sets. 2. **Model Training**: Trained a Linear Regression model. 3. **Prediction and Evaluation**: Predicted house prices and evaluated the model's performance with R-squared value. Here's a sample of the code: ```python import pandas as pd import numpy as np import matplotlib.pyplot as plt # Load dataset DataSet = pd.read_csv('test_house_data.csv') x = DataSet.iloc[:, :-1].values y = DataSet.iloc[:, -1].values # Split the dataset into training and testing sets from sklearn.model_selection import train_test_split x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=0) # Train the Linear Regression model from sklearn.linear_model import LinearRegression regressor = LinearRegression() regressor.fit(x_train, y_train) # Predict house prices for the test set y_pred = regressor.predict(x_test) np.set_printoptions(precision=2) print(np.concatenate((y_pred.reshape(len(y_pred), 1), y_test.reshape(len(y_test), 1)), 1)) # Evaluate the model's performance from sklearn.metrics import r2_score r2 = r2_score(y_test, y_pred) print(f'R-squared value: {r2}') # Predict the price of a specific house house_features = [[5236481927, 2, 2, 1750, 5560, 2, 3, 7, 1810, 1595]] predicted_price = regressor.predict(house_features) print(predicted_price) ``` Check out the full code and detailed analysis in my Colab notebook:(https://lnkd.in/eragWurx) Feel free to reach out if you have any questions or suggestions! #MachineLearning #DataScience #Python #LinearRegression #HousePrices

Hello Connections! 😊 Today, I am excited to share the completion of my latest project in the AI field: YouTube Analysis Using Python. 📊🔍 In this project, I delved deep into YouTube's data, leveraging advanced data analytics to uncover key insights and trends through real-time data scraping and analysis. Here’s a glimpse of what I have accomplished: https://lnkd.in/dJipG6Dj #DataScience #MachineLearning #Python #YouTubeAnalytics #AI #trending #ArtificialIntelligence

Unlock the power of data analysis with Python in Google Colab! 🚀 To perform regression and correlation analysis, follow these step-by-step instructions to streamline your workflow and gain valuable insights from your data. From data preparation to visualization, Google Colab makes it easy to run your analysis using Python libraries like Pandas, NumPy, Matplotlib, and Scikit-learn. Start optimizing your analysis today and boost your statistical skills! 💡 #DataScience #Python #GoogleColab #RegressionAnalysis #CorrelationAnalysis #MachineLearning #Statistics #DataVisualization #AI #TechTips #Colab #Programming #DataAnalysis https://lnkd.in/g-vykZF2

Imagine being able to talk to your data like you would to a colleague! The PandasAI library lets you do just that, using everyday language to explore, visualize, and refine your data. Ask questions, create visualizations, and even improve data quality with automatic feature generation. Behind the scenes, advanced language models (LLMs) work their magic to turn your natural language queries into actionable Python code and SQL commands. #PandasAI #DataAnalysis #LLM

Supercharge Preprocessing with Numba 🚀 Unlock massive performance gains in data preprocessing pipeline with Numba. This powerful tool uses just-in-time compilation to convert Python and NumPy code into optimized machine code. Key benefits: • 10-100x speedups for suitable operations • Easy integration with @jit decorator • Optimizes CPU-bound operations • Excels with numerical algorithms and hard-to-vectorize loops • Works best with NumPy arrays and scalar values • Measure gains with %timeit in Jupyter notebooks • Ideal for large datasets (e.g., million-element arrays) Numba shines where standard Python loops crawl. Run it in Colab: https://lnkd.in/gXnAzzrC

On Day 11 of the #Amdari21DaysDataChallenge, I performed data cleaning and exploratory data analysis, revealing intriguing patterns in power generation across the four seasons, with a significant number of rows showing zero power generation. I developed and trained various models, including linear regression, random forest, gradient-boosting regressor, and SVR. The random forest and gradient-boosting models demonstrated promising results, with reasonable mean squared error and mean absolute error. I further optimized these models through hyperparameter tuning, successfully identifying the optimal hyperparameters for improved performance. Below is the link to my workbook👇 https://lnkd.in/eZ55M4TX #Mariam_AlabiAmdariTODDC #DataAnalysis #DataScience #Python #Day11

📊 **Netflix Data Analysis with Python in Google Colab** 📊 I recently completed a project analyzing Netflix data using Python in Google Colab. This project involved data cleaning, exploratory data analysis, and visualization to uncover insights about Netflix's content library. Key findings: - Distribution of movies and TV shows - Trend of content added over the years - Content ratings and their distribution - Duration analysis of movies and TV shows Check out the detailed analysis https://lnkd.in/d45VufTk. #DataScience #Python #Netflix #DataAnalysis #GoogleColab

Lectures 10, 11, and 12 Highlights: Advanced AI with Sir Irfan Malik As-salamu alaykum! Today, in our Advanced AI Learning Journey, we are covering three essential lectures with Sir Irfan Malik. In these sessions, we will discuss: - Conditional statements - Loops (while loops and for loops) - User-defined functions in Python - Open Ai To enhance your learning experience, we've prepared a Colab notebook with practical exercises. Check out the link below and follow along with the exercises. 📚 Colab Notebook Link of Lecture 10 https://lnkd.in/d_fNBGFy 📚 Colab Notebook Link of Lecture 11 https://lnkd.in/dA67vN-P 📚 Colab Notebook Link of Lecture 12 https://lnkd.in/dEfsjd5v Let's continue building our Python skills together! #AI #Python #Functions #Loops #ConditionalStatements #LearningJourney #ZeonSolutions #TechSkills #ContinuousLearning

Notebook: https://lnkd.in/gyXaJQVv 💀 Proud to present my most recent project: A Python-Based Global Search Optimization Platform (GSOP) That Leads the Industry! 📊 This state-of-the-art notebook features: ✅ Modular design incorporating multiple optimization algorithms ✅ Parallel processing for improved performance ✅ Extensive statistical analysis and benchmarking ✅ Interactive visualizations and experiment configuration ✅ Model persistence for simple sharing and replication This tool can greatly enhance your optimization workflows, regardless of your field of expertise—data science, machine learning, or operations research. Look over the notebook and let me know what you think! How may you use this in your professional life? #MachineLearning #DataScience #Optimization #Python #OpenSource 🐉 If you think this is helpful, please like, comment, and share!