Data Science Portfolio - Kaggle Datasets & AI Projects | Artificial Intelligence

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网络:Data Analytics 印度10 484 技术与应用3 558...

📈 Telegram 频道 Data Science Portfolio - Kaggle Datasets & AI Projects | Artificial Intelligence 的分析概览

频道 Data Science Portfolio - Kaggle Datasets & AI Projects | Artificial Intelligence (@dataportfolio) 英语语言赛道中的是活跃参与者。目前社区聚集了 37 697 名订阅者，在 技术与应用 类别中位列第 3 558，并在印度地区排名第 10 484 位。

📊 受众指标与增长动态

自 невідомо 创建以来，项目保持高速增长，吸引了 37 697 名订阅者。

根据 06 七月, 2026 的最新数据，频道保持稳定运转。过去 30 天订阅人数变化为 -44，过去 24 小时变化为 13，整体触达仍然可观。

认证状态： 未认证
互动率 (ER)： 平均受众互动率为 3.51%。内容发布后 24 小时内通常能获得 0.97% 的反应，占订阅者总量。
帖子覆盖： 每篇帖子平均可获得 1 325 次浏览，首日通常累积 367 次浏览。
互动与反馈： 受众积极参与，单帖平均反应数为 7。
主题关注点： 内容集中在 learning, dataset, sql, link:-, analyst 等核心主题上。

📝 描述与内容策略

作者将该频道定位为表达主观观点的平台：
“Free Datasets For Data Science Projects & Portfolio Buy ads: https://telega.io/c/DataPortfolio For Promotions/ads: @coderfun @love_data”

凭借高频更新（最新数据采集于 07 七月, 2026），频道始终保持新鲜度与高覆盖。分析显示受众积极互动，使其成为 技术与应用 类别中的关键影响点。

37 697

订阅者

+1324 小时

+377 天

-4430 天

1 325

帖子浏览量

~ 36724 小时

~ 47248 小时

3.51%

参与率

无数据

每日帖子数

Ads index

beta

数据加载中...

吸引订阅者

七月 '26

+42

在0个频道中

六月 '26

+140

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五月 '26

+291

在0个频道中

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四月 '26

+159

在1个频道中

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三月 '26

+175

在1个频道中

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二月 '26

+451

在2个频道中

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一月 '26

+629

在1个频道中

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十二月 '25

+552

在2个频道中

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十一月 '25

+707

在0个频道中

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十月 '25

+607

在1个频道中

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九月 '25

+493

在2个频道中

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八月 '25

+792

在7个频道中

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七月 '25

+965

在3个频道中

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六月 '25

+1 338

在8个频道中

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五月 '25

+2 450

在10个频道中

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四月 '25

+3 541

在8个频道中

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三月 '25

+1 028

在9个频道中

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二月 '25

+1 039

在7个频道中

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一月 '25

+1 365

在13个频道中

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十二月 '24

+996

在7个频道中

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十一月 '24

+983

在12个频道中

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十月 '24

+966

在6个频道中

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九月 '24

+1 456

在4个频道中

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八月 '24

+1 429

在9个频道中

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七月 '24

+2 284

在8个频道中

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六月 '24

+2 641

在6个频道中

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五月 '24

+1 937

在2个频道中

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四月 '24

+2 081

在2个频道中

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三月 '24

+2 387

在3个频道中

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二月 '24

+1 473

在0个频道中

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一月 '24

+4 649

在5个频道中

日期	订阅者增长	提及	频道
07 七月	0
06 七月	+13
05 七月	0
04 七月	+8
03 七月	+2
02 七月	+13
01 七月	+6

频道帖子

🎯 2026 Free Course 👇 1/ Python for Beginners (Microsoft): https://learn.microsoft.com/en-us/shows/intro-to-python-development/ 2/ SQL for Data Science (IBM via edX): https://www.edx.org/learn/data-science/ibm-sql-for-data-science 3/ Java Programming (Infosys Springboard) https://placementdriveinsta.in/infosys-free-java-course-with-certificate/ 4/ AI Fundamentals - AI-900 (Microsoft) https://learn.microsoft.com/en-us/training/paths/introduction-to-ai-on-azure/ 5/ Fundamentals of Machine Learning (NVIDIA DLI) https://www.coursera.org/learn/fundamentals-of-machine-learning 6/ AWS Cloud Practitioner Essentials (AWS) https://explore.skillbuilder.aws/learn/course/external/view/elearning/134/aws-cloud-practitioner-essentials 7/ Get Started with Microsoft Data Analytics https://learn.microsoft.com/en-us/training/modules/get-started-data-analytics/ 8/ Data Science Foundations (IBM SkillsBuild) https://skillsbuild.org/students/course-catalog/data-science

2	✅ Python for Data Science – Part 1: NumPy Interview Q&A 📊 🔹 1. What is NumPy and why is it important? NumPy (Numerical Python) is a powerful Python library for numerical computing. It supports fast array operations, broadcasting, linear algebra, and random number generation. It’s the backbone of many data science libraries like Pandas and Scikit-learn. 🔹 2. Difference between Python list and NumPy array Python lists can store mixed data types and are slower for numerical operations. NumPy arrays are faster, use less memory, and support vectorized operations, making them ideal for numerical tasks. 🔹 3. How to create a NumPy array import numpy as np arr = np.array([1, 2, 3]) 🔹 4. What is broadcasting in NumPy? Broadcasting lets you perform operations on arrays of different shapes. For example, adding a scalar to an array applies the operation to each element. 🔹 5. How to generate random numbers Use np.random.rand() for uniform distribution, np.random.randn() for normal distribution, and np.random.randint() for random integers. 🔹 6. How to reshape an array Use .reshape() to change the shape of an array without changing its data. Example: arr.reshape(2, 3) turns a 1D array of 6 elements into a 2x3 matrix. 🔹 7. Basic statistical operations Use functions like mean(), std(), var(), sum(), min(), and max() to get quick stats from your data. 🔹 8. Difference between zeros(), ones(), and empty() np.zeros() creates an array filled with 0s, np.ones() with 1s, and np.empty() creates an array without initializing values (faster but unpredictable). 🔹 9. Handling missing values Use np.nan to represent missing values and np.isnan() to detect them. Example: arr = np.array([1, 2, np.nan]) np.isnan(arr) # Output: [False False True] 🔹 10. Element-wise operations NumPy supports element-wise addition, subtraction, multiplication, and division. Example: a = np.array([1, 2, 3]) b = np.array([4, 5, 6]) a + b # Output: [5 7 9] 💡 Pro Tip: NumPy is all about speed and efficiency. Mastering it gives you a huge edge in data manipulation and model building. Double Tap ❤️ For More	1 325
3	Real-world Data Science projects ideas: 💡📈 1. Credit Card Fraud Detection 📍 Tools: Python (Pandas, Scikit-learn) Use a real credit card transactions dataset to detect fraudulent activity using classification models. Skills you build: Data preprocessing, class imbalance handling, logistic regression, confusion matrix, model evaluation. 2. Predictive Housing Price Model 📍 Tools: Python (Scikit-learn, XGBoost) Build a regression model to predict house prices based on various features like size, location, and amenities. Skills you build: Feature engineering, EDA, regression algorithms, RMSE evaluation. 3. Sentiment Analysis on Tweets or Reviews 📍 Tools: Python (NLTK / TextBlob / Hugging Face) Analyze customer reviews or Twitter data to classify sentiment as positive, negative, or neutral. Skills you build: Text preprocessing, NLP basics, vectorization (TF-IDF), classification. 4. Stock Price Prediction 📍 Tools: Python (LSTM / Prophet / ARIMA) Use time series models to predict future stock prices based on historical data. Skills you build: Time series forecasting, data visualization, recurrent neural networks, trend/seasonality analysis. 5. Image Classification with CNN 📍 Tools: Python (TensorFlow / PyTorch) Train a Convolutional Neural Network to classify images (e.g., cats vs dogs, handwritten digits). Skills you build: Deep learning, image preprocessing, CNN layers, model tuning. 6. Customer Segmentation with Clustering 📍 Tools: Python (K-Means, PCA) Use unsupervised learning to group customers based on purchasing behavior. Skills you build: Clustering, dimensionality reduction, data visualization, customer profiling. 7. Recommendation System 📍 Tools: Python (Surprise / Scikit-learn / Pandas) Build a recommender system (e.g., movies, products) using collaborative or content-based filtering. Skills you build: Similarity metrics, matrix factorization, cold start problem, evaluation (RMSE, MAE). 👉 Pick 2–3 projects aligned with your interests. 👉 Document everything on GitHub, and post about your learnings on LinkedIn. Here you can find the project datasets: https://whatsapp.com/channel/0029VbAbnvPLSmbeFYNdNA29 React ❤️ for more	0
4	If I need to teach someone data analytics from the basics, here is my strategy: 1. I will first remove the fear of tools from that person 2. i will start with the excel because it looks familiar and easy to use 3. I put more emphasis on projects like at least 5 to 6 with the excel. because in industry you learn by doing things 4. I will release the person from the tutorial hell and move into a more action oriented person 5. Then I move to the sql because every job wants it , even with the ai tools you need strong understanding for it if you are going to use it daily 6. After strong understanding, I will push the person to solve 100 to 150 Sql problems from basic to advance 7. It helps the person to develop the analytical thinking 8. Then I push the person to solve 3 case studies as it helps how we pull the data in the real life 9. Then I move the person to power bi to do again 5 projects by using either sql or excel files 10. Now the fear is removed. 11. Now I push the person to solve unguided challenges and present them by video recording as it increases the problem solving, communication and data story telling skills 12. Further it helps you to clear case study round given by most of the companies 13. Now i help the person how to present them in resume and also how these tools are used in real world. 14. You know the interesting fact, all of above is present free in youtube and I also mentor the people through existing youtube videos. 15. But people stuck in the tutorial hell, loose motivation , stay confused that they are either in the right direction or not. 16. As a personal mentor , I help them to get of the tutorial hell, set them in the right direction and they stay motivated when they start to see the difference before amd after mentorship I have curated best 80+ top-notch Data Analytics Resources 👇👇 https://topmate.io/analyst/861634 Hope this helps you 😊	0
5	🔹 DATA SCIENCE – INTERVIEW REVISION SHEET 1️⃣ What is Data Science? > “Data science is the process of using data, statistics, and machine learning to extract insights and build predictive or decision-making models.” Difference from Data Analytics: • Data Analytics → past present (what/why) • Data Science → future automation (what will happen) 2️⃣ Data Science Lifecycle (Very Important) 1. Business problem understanding 2. Data collection 3. Data cleaning preprocessing 4. Exploratory Data Analysis (EDA) 5. Feature engineering 6. Model building 7. Model evaluation 8. Deployment monitoring Interview line: > “I always start from business understanding, not the model.” 3️⃣ Data Types • Structured → tables, SQL • Semi-structured → JSON, logs • Unstructured → text, images 4️⃣ Statistics You MUST Know • Central tendency: Mean, Median (use when outliers exist) • Spread: Variance, Standard deviation • Correlation ≠ causation • Normal distribution • Skewness (income → right skewed) 5️⃣ Data Cleaning Preprocessing Steps you should say in interviews: 1. Handle missing values 2. Remove duplicates 3. Treat outliers 4. Encode categorical variables 5. Scale numerical data Scaling: • Min-Max → bounded range • Standardization → normal distribution 6️⃣ Feature Engineering (Interview Favorite) > “Feature engineering is creating meaningful input variables that improve model performance.” Examples: • Extract month from date • Create customer lifetime value • Binning age groups 7️⃣ Machine Learning Basics • Supervised learning: Regression, Classification • Unsupervised learning: Clustering, Dimensionality reduction 8️⃣ Common Algorithms (Know WHEN to use) • Regression: Linear regression → continuous output • Classification: Logistic regression, Decision tree, Random forest, SVM • Unsupervised: K-Means → segmentation, PCA → dimensionality reduction 9️⃣ Overfitting vs Underfitting • Overfitting → model memorizes training data • Underfitting → model too simple Fixes: • Regularization • More data • Cross-validation 🔟 Model Evaluation Metrics • Classification: Accuracy, Precision, Recall, F1 score, ROC-AUC • Regression: MAE, RMSE Interview line: > “Metric selection depends on business problem.” 1️⃣1️⃣ Imbalanced Data Techniques • Class weighting • Oversampling / undersampling • SMOTE • Metric preference: Precision, Recall, F1, ROC-AUC 1️⃣2️⃣ Python for Data Science Core libraries: • NumPy • Pandas • Matplotlib / Seaborn • Scikit-learn Must know: • loc vs iloc • Groupby • Vectorization 1️⃣3️⃣ Model Deployment (Basic Understanding) • Batch prediction • Real-time prediction • Model monitoring • Model drift Interview line: > “Models must be monitored because data changes over time.” 1️⃣4️⃣ Explain Your Project (Template) > “The goal was . I cleaned the data using . I performed EDA to identify . I built model and evaluated using . The final outcome was .” 1️⃣5️⃣ HR-Style Data Science Answers Why data science? > “I enjoy solving complex problems using data and building models that automate decisions.” Biggest challenge: “Handling messy real-world data.” Strength: “Strong foundation in statistics and ML.” 🔥 LAST-DAY INTERVIEW TIPS • Explain intuition, not math • Don’t jump to algorithms immediately • Always connect model → business value • Say assumptions clearly Double Tap ♥️ For More	0
6	Here is the list of few projects (found on kaggle). They cover Basics of Python, Advanced Statistics, Supervised Learning (Regression and Classification problems) & Data Science Please also check the discussions and notebook submissions for different approaches and solution after you tried yourself. 1. Basic python and statistics Pima Indians :- https://www.kaggle.com/uciml/pima-indians-diabetes-database Cardio Goodness fit :- https://www.kaggle.com/saurav9786/cardiogoodfitness Automobile :- https://www.kaggle.com/toramky/automobile-dataset 2. Advanced Statistics Game of Thrones:-https://www.kaggle.com/mylesoneill/game-of-thrones World University Ranking:-https://www.kaggle.com/mylesoneill/world-university-rankings IMDB Movie Dataset:- https://www.kaggle.com/carolzhangdc/imdb-5000-movie-dataset 3. Supervised Learning a) Regression Problems How much did it rain :- https://www.kaggle.com/c/how-much-did-it-rain-ii/overview Inventory Demand:- https://www.kaggle.com/c/grupo-bimbo-inventory-demand Property Inspection predictiion:- https://www.kaggle.com/c/liberty-mutual-group-property-inspection-prediction Restaurant Revenue prediction:- https://www.kaggle.com/c/restaurant-revenue-prediction/data IMDB Box office Prediction:-https://www.kaggle.com/c/tmdb-box-office-prediction/overview b) Classification problems Employee Access challenge :- https://www.kaggle.com/c/amazon-employee-access-challenge/overview Titanic :- https://www.kaggle.com/c/titanic San Francisco crime:- https://www.kaggle.com/c/sf-crime Customer satisfcation:-https://www.kaggle.com/c/santander-customer-satisfaction Trip type classification:- https://www.kaggle.com/c/walmart-recruiting-trip-type-classification Categorize cusine:- https://www.kaggle.com/c/whats-cooking 4. Some helpful Data science projects for beginners https://www.kaggle.com/c/house-prices-advanced-regression-techniques https://www.kaggle.com/c/digit-recognizer https://www.kaggle.com/c/titanic 5. Intermediate Level Data science Projects Black Friday Data : https://www.kaggle.com/sdolezel/black-friday Human Activity Recognition Data : https://www.kaggle.com/uciml/human-activity-recognition-with-smartphones Trip History Data : https://www.kaggle.com/pronto/cycle-share-dataset Million Song Data : https://www.kaggle.com/c/msdchallenge Census Income Data : https://www.kaggle.com/c/census-income/data Movie Lens Data : https://www.kaggle.com/grouplens/movielens-20m-dataset Twitter Classification Data : https://www.kaggle.com/c/twitter-sentiment-analysis2 Share with credits: https://t.me/sqlproject ENJOY LEARNING 👍👍	0

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