Python Data Analysis Day 47: Text Data Processing with Scikit-learn

#20DaysChallenge – Day 7 📊 Still on data visualization but today I'll like to talk about tools used for data visualization. In data science, tools like Matplotlib and Seaborn in Python help turn data into clear and meaningful visuals. With these tools, we can create: Bar charts, Line graphs, Histograms, Pie charts. This makes it easier to understand patterns and trends in a dataset. I’m beginning to see how powerful visualization is for communicating insights from data. The learning continues, one step at a time. 🚀 #AfricaAgility #AIJourney #DataVisualization #Python #WomenInTech #WomenInSTEM #IWD #AI #ML

𝐏𝐫𝐞𝐩𝐫𝐨𝐜𝐞𝐬𝐬 𝐃𝐚𝐭𝐚 𝐰𝐢𝐭𝐡 𝐒𝐤𝐥𝐞𝐚𝐫𝐧 𝐃𝐚𝐲 48: 50 𝐃𝐚𝐲𝐬 𝐨𝐟 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝐰𝐢𝐭𝐡 𝐏𝐲𝐭𝐡𝐨𝐧 This session focused on preparing data for supervised machine learning by encoding the target variable into numeric form, separating features and labels, splitting the dataset into training and test sets to evaluate model generalization, and standardizing features using StandardScaler to ensure consistent scaling and improved model performance. 𝐎𝐬𝐭𝐢𝐧𝐚𝐭𝐨 𝐑𝐢𝐠𝐨𝐫𝐞 #Python #NumPy #DataAnalysis #DataScience #MachineLearning #ArtificialIntelligence #DataAnalytics #LearnInPublic #GitHub #Data #TechCommunity #DailyPractice #Consistency #DataDriven #50_days_of_data_analysis_with_python #SQL #Learning #ostinatorigore

📊 Exploring Data Visualization with NumPy, Matplotlib & Seaborn Today I practiced creating different statistical visualizations while learning Python data analysis. I experimented with: • Distribution plots using sns.displot() • Count plots to visualize frequency • Kernel Density Estimation (KDE) for smooth distribution curves • Generating random data using NumPy (Normal & Binomial distributions) It was interesting to see how different distributions behave visually and how visualization helps in understanding data patterns better. Libraries used: NumPy, Matplotlib, and Seaborn. Learning step by step in the journey of Data Science 🚀 #Python #NumPy #Matplotlib #Seaborn #DataVisualization #LearningJourney

This week in our Machine Learning lab, we explored Principal Component Analysis (PCA), one of the most powerful dimensionality reduction techniques in data science. We learned how PCA helps in: • Reducing high-dimensional data into fewer components • Preserving maximum variance in the dataset • Improving visualization and computational efficiency • Removing multicollinearity between features In the lab, we: Standardized the dataset Computed covariance matrices Understood eigenvalues & eigenvectors Transformed features into principal components using Python It was interesting to see how complex datasets can be simplified while still retaining the most important information. Understanding PCA gave us deeper insight into how preprocessing impacts model performance and why scaling plays a crucial role before applying the algorithm. Excited to apply dimensionality reduction techniques in future ML projects 🚀 #MachineLearning #PCA #DataScience #ArtificialIntelligence #Python #StudentLife #LearningJourney

Today’s Learning 📈 Explored the role of hypothesis testing (H₀ vs H₁, p-values, significance levels) and sampling methods in data science. Learned how these statistical tools help validate assumptions, reduce bias, and support reliable decision-making in real-world analytics and machine learning workflows. #DataScience #Statistics #HypothesisTesting #Sampling #EDA #MachineLearning #LearningJourney #Python #SQL

Exploratory Data Analysis on Titanic Dataset 🚢 Exploratory Data Analysis (EDA) on Titanic Dataset I performed data cleaning and exploratory data analysis using Python. 🔹 Handled missing values (Age, Embarked) 🔹 Removed irrelevant columns (Cabin) 🔹 Analyzed survival patterns 🔹 Visualized relationships between variables 📊 Key Insights: Females had a significantly higher survival rate than males 1st class passengers had better survival chances Passenger class and gender strongly influenced survival 🛠 Tools Used: Python | Pandas | Matplotlib | Seaborn | VS Code #Python #DataScience #EDA #MachineLearning #Kaggle #ProdigyInfotech #Learning #BTech

🚀 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗧𝗲𝗰𝗵: 𝗣𝘆𝘁𝗵𝗼𝗻 | 𝗗𝗮𝘁𝗮 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 | 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 🤖📊 📌 Building skills in Python programming 💻 📌 Turning raw data into insights with data visualization 📈 📌 Exploring agentic AI and intelligent automation 🧠 📌 Writing cleaner code and smarter solutions 🚀 📌 Continuous learning for a data-driven future 🌍 Continuous learning is the key to staying relevant in today’s 𝗔𝗜-𝗗𝗿𝗶𝘃𝗲𝗻 𝗪𝗼𝗿𝗹𝗱 🌍 Let’s connect if you’re also exploring tech, AI and data 🚀 #Python #DataVisualization #AgenticAI #ArtificialIntelligence #DataScience #ContinuousLearning #TechJourney #Geodatascientist #Data #DataAnalytics

I recently practiced implementing the K-Means Clustering algorithm using Python to strengthen my understanding of unsupervised machine learning techniques. In this practice notebook, I: • Generated synthetic data using make_blobs • Performed data preprocessing using StandardScaler • Applied the K-Means algorithm from Scikit-learn • Used the Elbow Method to determine the optimal number of clusters • Visualized clustering results using Matplotlib and Seaborn This exercise helped me better understand how clustering works, how to scale data before training, and how inertia is used to evaluate cluster performance. 🔧 Tools & Libraries Used: Python | Pandas | NumPy | Scikit-learn | Matplotlib | Seaborn | Jupyter Notebook This is part of my machine learning practice while learning data science concepts. Looking forward to exploring more algorithms and real-world datasets. #MachineLearning #DataScience #KMeans #UnsupervisedLearning #Python #LearningJourney #DataAnalytics

🚀 New Data Science Project I recently completed a machine learning project focused on detecting risky purchase transactions that may lead to product returns. The project involved exploring an imbalanced dataset with more than 11,000 transactions and 153 features. Several models were tested including Logistic Regression, Random Forest, Gradient Boosting, and XGBoost. Due to severe class imbalance, traditional classification models struggled to detect rare events. Reframing the problem using anomaly detection with Isolation Forest significantly improved the detection of risky transactions. Tools used: Python | Pandas | Scikit-learn | XGBoost | Data Visualization You can check the full project on GitHub: [https://lnkd.in/dKmjKz-s] #DataScience #MachineLearning #Python #Analytics

Exploring how modern AI systems store and retrieve knowledge efficiently. Hands-on experience with Pinecone vector databases, covering: • Pinecone and vector database fundamentals • Vector manipulation in Python for embeddings • Performance tuning & AI applications Learned vector index optimization, explored multi-tenant architectures, and built semantic search and RAG projects using Pinecone with the OpenAI API. #AIEngineer #AIDeveloper #VectorDatabases #Pinecone #Embeddings #SemanticSearch #RAG #OpenAI #Python #AgenticAI #BackendDevelopment