Web Scraping for Data Scientists: Automating Data Collection

𝗖𝗿𝗲𝗮𝘁𝗲 𝗘𝘅𝗽𝗹𝗮𝗶𝗻𝗮𝗯𝗹𝗲 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗠𝗼𝗱𝗲𝗹𝘀! Machine learning models usually have low explainability, hence making it difficult to understand their predictions. This is a serious obstacle in many cases, including industries where black box models are unacceptable. Shapash is a Python library that lets you understand machine learning models by providing an interactive web dashboard. Furthermore, shapash can also be used to generate reports, hence being a significantly useful tool for data scientists and analysts! Visit the link below for more information, and make sure to follow me for regular data science content. 𝗦𝗵𝗮𝗽𝗮𝘀𝗵 𝗹𝗶𝗯𝗿𝗮𝗿𝘆 𝘄𝗲𝗯𝘀𝗶𝘁𝗲: https://lnkd.in/dDiid5Vj 𝗟𝗲𝗮𝗿𝗻 𝗠𝗟 𝗮𝗻𝗱 𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴: https://lnkd.in/dyByK4F #datascience #python #deeplearning #machinelearning

𝗣𝘆𝘁𝗵𝗼𝗻 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 ✅ Core Python: is vs ==, dict key checks, list comprehensions, duplicates ✅ Advanced basics: memoization, generators vs iterators, decorators, *args/**kwargs ✅ Data work: pandas groupby, apply, transform, pipe, query, MultiIndex ✅ NumPy: broadcasting and vectorization vs loops ✅ Visualization: Matplotlib dual axes, Seaborn vs Matplotlib ✅ Real-world: custom exceptions + logging, log parsing, data cleaning, login grouping Interview angle: many answers include why, when to use, and tips that makes it more useful than a simple Q&A sheet. Best for: Python beginners moving into data engineering, analytics, or ML roles. #Python #InterviewQuestions #Pandas #NumPy #DataEngineering #Programming

Machine Learning Data Visualization using seaborn #machinelearning #datascience #datavisualization #seaborn Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics Seaborn builds on top of matplotlib and integrates closely with pandas data structures. Seaborn helps you explore and understand your data. Its plotting functions operate on dataframes and arrays containing whole datasets and internally perform the necessary semantic mapping and statistical aggregation to produce informative plots. Its dataset-oriented, declarative API lets you focus on what the different elements of your plots mean, rather than on the details of how to draw them. https://lnkd.in/gYsJ_sVm

Turning Raw Data into Insights in Seconds(key skill for any data scientist) I built a simple yet powerful Python tool that helps analyze data distribution instantly.This is a small step, but a strong foundation Understanding how data is distributed (skewed, symmetric, etc.) can be confusing and time-consuming for beginners. I created a Python script where you simply pass an array, and it automatically calculates: ✔ Mean ✔ Median ✔ Mode ✔ Data distribution (Right Skewed / Left Skewed / Symmetric) Please don’t hesitate to reach out if you’d like the full code for practice purposes — feel free to DM me! @Zeeshan Ali — would love your feedback on this! #DataScience #Python #Statistics #Coding#Talha Ammar

🚀 Day 64/100 – Python, Data Analytics & Machine Learning Journey 🤖 Module 3: Machine Learning 📚 Today’s Learning: • Model Saving & Loading using joblib • Exporting trained models Today, I explored the concept of a Machine Learning Pipeline, which helps in organizing and automating the workflow of building a machine learning model. In simple terms, a pipeline allows us to connect multiple steps such as data preprocessing, feature scaling, and model training into a single streamlined process. Instead of handling each step separately, everything is executed sequentially, making the code cleaner, more efficient, and less error-prone. One of the key advantages I learned is consistency the same transformations applied to training data are automatically applied to testing data. This ensures reliability and prevents data leakage. I also learned how to save trained models using joblib, which is useful for deploying models without retraining them every time. Overall, pipelines improve code readability, reusability, and make real-world deployment much easier. The learning journey continues as I explore more advanced machine learning concepts and their practical implementations. 📌 Code & Notes: https://lnkd.in/dmFHqCrK #100DaysOfPython #MachineLearning #AIML #Python #LearningInPublic #DataScience

🚀 Day 53/100 – Python, Data Analytics & Machine Learning Journey 🤖 Module 3: Machine Learning 📚 Today’s Learning: Classification Metrics – Confusion Matrix, Accuracy, Precision, Recall, F1 Score Regression Metrics – Mean Squared Error (MSE), R² Score Today, I focused on understanding how to evaluate machine learning models effectively using different performance metrics. In classification, I learned about metrics like Accuracy, Precision, Recall, and F1 Score along with the Confusion Matrix, which help in analyzing model predictions in detail. For regression, I explored Mean Squared Error (MSE) and R² Score, which are essential to measure how well a model predicts continuous values. Understanding these metrics is important to improve model performance and make better data-driven decisions. The learning journey continues as I dive deeper into machine learning concepts and real-world applications. 📌 Code & Notes: https://lnkd.in/dmFHqCrK #100DaysOfPython #MachineLearning #AIML #Python #LearningInPublic #DataScience

🚀 Day 50/100 – Python, Data Analytics & Machine Learning Journey 🤖 Module 3: Machine Learning 📚 Today’s Learning: Supervised Learning – Regression Algorithm 2: Decision Tree Regression Today I explored Decision Tree Regression, a supervised machine learning algorithm used to predict continuous values by learning decision rules from the data. Unlike linear models, Decision Tree Regression works by splitting the dataset into smaller subsets based on feature values, forming a tree-like structure. Each split helps the model make more precise predictions by grouping similar data points together. One of the key advantages of Decision Tree Regression is its ability to capture non-linear relationships in the data and provide easy-to-understand decision rules. This algorithm is widely used in applications such as price prediction, demand forecasting, risk analysis, and customer behavior modeling. The learning journey continues as I explore more regression algorithms and their real-world applications. 📌 Code & Notes: https://lnkd.in/dmFHqCrK #100DaysOfPython #MachineLearning #AIML #Python #LearningInPublic #DataScience

🚀 Day 63/100 – Python, Data Analytics & Machine Learning Journey 🤖 Module 3: Machine Learning 📚 Today’s Learning: • Machine Learning Pipeline Today, I explored the concept of a Machine Learning Pipeline, which helps in organizing and automating the workflow of building a machine learning model. In simple terms, a pipeline allows us to connect multiple steps such as data preprocessing, feature scaling, and model training into a single streamlined process. Instead of handling each step separately, everything is executed in sequence, making the code cleaner and more efficient. I learned that pipelines are especially useful for ensuring consistency. The same transformations applied to the training data are automatically applied to the testing data, which helps avoid errors and improves model reliability. A typical pipeline may include steps like: 1. Data preprocessing 2. Feature scaling 3. Model training Using pipelines also improves code readability and reusability, making it easier to deploy models in real world applications. The learning journey continues as I explore more advanced machine learning concepts and their practical implementations. 📌 Code & Notes: https://lnkd.in/dmFHqCrK #100DaysOfPython #MachineLearning #AIML #Python #LearningInPublic #DataScience

Today I practiced Sorting data using Pandas in Python 📊🐍 Sorting is very useful when analyzing datasets to identify trends, top values, or patterns. Two important functions: 🔹 sort_values() – Sort data based on column values 🔹 sort_index() – Sort data based on index Example: df.sort_values(by="Sales", ascending=False) df.sort_index() This helps quickly identify top-performing products, highest sales, or important insights in a dataset. Small concepts like these make data analysis much more efficient. Continuing to build strong foundations in Python for Data Analytics. #Python #Pandas #DataAnalytics #LearningJourney

🚀 Day 54/100 – Python, Data Analytics & Machine Learning Journey 🤖 Module 3: Machine Learning 📚 Today’s Learning: • Cross Validation Today, I focused on understanding how to evaluate machine learning models effectively using different performance metrics and validation techniques. I explored Cross Validation (K-Fold), a powerful technique that helps in evaluating model performance by splitting the dataset into multiple folds and training/testing the model multiple times. This ensures better reliability and reduces the chances of overfitting. In classification, I learned about metrics like Accuracy, Precision, Recall, and F1 Score, along with the Confusion Matrix, which help in analyzing model predictions in detail. For regression, I explored Mean Squared Error (MSE) and R² Score, which are essential to measure how well a model predicts continuous values. Understanding these metrics and validation techniques is important to improve model performance and make better data-driven decisions. The learning journey continues as I dive deeper into machine learning concepts and real-world applications. 🚀 📌 Code & Notes: https://lnkd.in/dmFHqCrK #100DaysOfPython #MachineLearning #AIML #Python #LearningInPublic #DataScience