Time Series Data Splitting in Python for Sales Forecasting

📊 NumPy Cheat Sheet – Must Know for Data Science If you're learning Python for Data Science / Machine Learning, mastering NumPy is non-negotiable. Here’s a quick revision guide 👇 🔍 Core Concepts: 🧱 Array Creation • np.array() • np.arange() • np.linspace() • np.zeros() / np.ones() 🔄 Array Operations • Reshape & Flatten • Indexing & Slicing • Concatenation & Splitting 📐 Mathematical Operations • np.mean() • np.sum() • np.std() • Dot Product (np.dot()) ⚡ Broadcasting & Vectorization • Perform operations without loops • Faster computation 🚀 🎲 Random Module • np.random.rand() • np.random.randint() • np.random.normal() 📊 Linear Algebra • Matrix Multiplication • Determinant & Inverse • Eigenvalues & Eigenvectors 💡 Key Takeaways: ✔ NumPy = Backbone of ML & Data Science ✔ Vectorization improves performance drastically ✔ Essential for libraries like Pandas, Scikit-learn, TensorFlow 🎯 Perfect for interview prep + quick revision #NumPy #Python #DataScience #MachineLearning #AI #Coding #LearnPython #Tech

🚀 AI/ML Series – NumPy Day 1/3: Arrays Made Easy After mastering Pandas, it’s time to learn the backbone of Data Science: NumPy 🔥 📌 What is NumPy? NumPy stands for Numerical Python and is used for fast mathematical operations on arrays. Why is it important? ✅ Faster than Python lists ✅ Handles large numerical data efficiently ✅ Used in Machine Learning & Deep Learning ✅ Supports arrays, matrices & vectorized operations 📌 In Today’s Post, We Cover: ✅ Creating Arrays ✅ 1D vs 2D Arrays ✅ shape, ndim, dtype ✅ Indexing & Slicing ✅ Basic Math Operations ✅ Why NumPy is faster than lists 📌 Example: import numpy as np arr = np.array([10, 20, 30, 40, 50]) print(arr) print(arr.shape) print(arr[0:3]) 💡 If Pandas is for tables, NumPy is for numbers. 🔥 This is Day 1/3 of NumPy Series Tomorrow: Advanced NumPy Tricks (reshape, random, broadcasting) 📌 Save this post if you're learning Data Science. 💬 Have you used NumPy before? #AI #MachineLearning #DataScience #Python #NumPy #Pandas #Coding #Analytics

🚀 Understanding OneHotEncoder, Sparse Matrix & Subplots (Matplotlib) — My Learning Today Today I explored some important concepts in Data Science & ML preprocessing: 🔹 OneHotEncoder Converts categorical data into numerical form (0/1) Each category becomes a separate column Helps models understand non-numeric data properly 🔹 Sparse Matrix vs Array OneHotEncoder returns a sparse matrix (memory efficient) Models can directly use it ✅ But for visualization or DataFrame → we use .toarray() 👉 Key insight: Sparse = machine-friendly Array/DataFrame = human-friendly 🔹 Index Importance in Pandas While creating new DataFrames, matching index is crucial Wrong index → data misalignment ❌ 🔹 Matplotlib Subplots (111) 111 means → 1 row, 1 column, 1st position Position = location of plot in grid 💡 Biggest takeaway: Understanding why behind each step is more important than just writing code. #MachineLearning #DataScience #Python #LearningInPublic #BCA #AI #StudentJourney

Day 2: Mastering the Architecture of Data – Python Data Structures! 🏗️ for Gen AI Revision After laying the foundation yesterday, Day 2 was all about the building blocks. In Gen AI development, how you store and manipulate data (tokens, embeddings, prompts) defines the efficiency of your model. Today was a deep dive into Python Data Structures. It’s not just about knowing list or dict; it’s about knowing why and where to use them for memory efficiency and speed. 🧠 What I Mastered Today: Strings & Immutability: Deep dive into slicing, advanced formatting (f-strings), and understanding why strings are immutable—a key concept when handling large text datasets for LLMs. Lists & Tuples: Beyond basic indexing. Focused on list comprehensions for clean code and using tuples for data integrity (immutable sequences). Sets for Performance: Leveraging hash-based lookups for unique element extraction and mathematical set operations (union/intersection)—crucial for data preprocessing. Dictionaries (The Powerhouse): Building efficient word frequency counters and nested structures. Understanding O(1) complexity for fast data retrieval. I didn't just read theory; I solved 15+ mini-problems ranging from character frequency analysis to complex list flattening—all without using external libraries to keep the logic raw and sharp. 💻 GitHub Progress: I’ve pushed the practice.py file with all 15+ solved challenges to my repo: day02_data_structures/ 🔗 https://lnkd.in/gikzc-K8 The journey to an MNC as a Gen AI dev is about consistency. Two days down, 88 to go. 🚀 #Python #DataStructures #GenAI #GenerativeAI #100DaysOfCode #AIDevelopment #TechJourney #MNCGoal #RevisionSeries #BackendDevelopment

Everyone talks about AI models. But here’s where it actually starts 👇 Loading and understanding your data. Today, I worked on the foundation of any data project: 📂 Importing datasets using Python 🔍 Previewing data with .head() 📊 Inspecting structure, shape, and overall quality Sounds simple? It is. But skipping this step is where most mistakes begin. What I realized today: 👉 The first few lines of your dataset can tell you more than you think 👉 Understanding data structure early saves hours later 👉 Good analysis isn’t about rushing — it’s about asking better questions Before building anything complex, I’m focusing on getting comfortable with the data itself. Because at the end of the day: Better data understanding = better decisions. This is part of my ongoing journey into data analytics and machine learning — building skills one practical step at a time. If you’re in this space: What’s the first thing you check when you load a new dataset? #DataScience #Python #DataAnalytics #MachineLearning #LearningInPublic #TechJourney #Data #AI UNLOX® Girish Kumar

🚀 Built an AI Data Analyzer using Python & Streamlit I developed an AI-powered application that converts raw, unstructured data into meaningful insights. 🔍 Key Features: • Supports CSV, Excel, TXT, PDF • AI cleans and structures raw data • Generates tables and visualizations (Bar & Pie Charts) • Provides AI-based insights • Exports final results as a PDF report ⚡ Workflow: Upload → AI Cleaning → Data Preview → Charts → AI Insights → PDF Report 🎥 Demo Video: https://lnkd.in/gD5h_REg 📂 GitHub Repo: https://lnkd.in/g2g94Vq3 💼 Let’s connect: https://lnkd.in/gbEr9cKj #AI #MachineLearning #DataAnalysis #Python #Streamlit #Projects #DataScience

Just Built & Deployed My Machine Learning Project From dataset to trained ML model to deployed prediction application. I developed a California House Price Prediction System using Machine Learning and deployed it with Streamlit. The system predicts house prices based on important housing features such as: • Median Income • House Age • Total Rooms • Population • Latitude & Longitude Model Used RandomForestRegressor Tech Stack • Python • Pandas & NumPy • Scikit-learn • Random Forest Regression • Streamlit (for deployment) Live Demo https://lnkd.in/dW8FuqCU Source Code https://lnkd.in/dB7Z4cgx Model Performance Training Set Results MAE: 25,180 MSE: 1,431,165,852 RMSE: 37,830 Test Set Results MAE: 34,073 MSE: 2,587,975,219 RMSE: 50,872 R² Score: 0.81 These results indicate that the model captures housing price patterns reasonably well and generalizes effectively to unseen data. What I learned from this project • Data preprocessing and feature engineering • Training and evaluating regression models • Understanding error metrics such as MAE, MSE, RMSE, and R² • Deploying machine learning models using Streamlit Next Improvements • Hyperparameter tuning • Experimenting with advanced models such as XGBoost and Gradient Boosting • Adding visualization dashboards for deeper insights Feedback and suggestions are welcome. #MachineLearning #DataScience #MLEngineer #Python #AIProjects #Streamlit #DataAnalytics #ArchTechnologies

🚀 Learn with Soumava | Series 01: Mastering the Foundation of AI with NumPy 📊 Beyond the Loop: Why NumPy is a Game-Changer for ETL & AI As an ETL professional transitioning deeper into AI and Data Science, I’ve realized that the biggest "productivity unlock" isn't just knowing Python—it’s mastering NumPy. In traditional testing, we often rely on row-by-row logic. However, in the world of High-Volume Data and AI, efficiency is everything. Using NumPy’s Vectorized Operations, we can process millions of data points 50x to 100x faster than standard Python lists. I’ve put together a Hands-on Google Colab Notebook that covers the essentials: 🔹 The "Axis" Secret: How to calculate means and sums across rows vs. columns (Axis 0 vs. Axis 1). 🔹 Boolean Masking: Filtering millions of rows of data without a single if statement. 🔹 Broadcasting: Performing complex math across different array shapes automatically. 🔹 Statistical Aggregates: Using std, median, and mean to detect data drift and outliers. Check out the full walkthrough in the document below! What’s your go-to NumPy trick for data validation? Let’s discuss in the comments. #Python #NumPy #DataEngineering #ETLTesting #AI #DataScience ##MachineLearning #TechLearning

🚀 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐏𝐲𝐭𝐡𝐨𝐧 – 𝐃𝐚𝐭𝐚 𝐒𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐬, 𝐓𝐲𝐩𝐞 𝐂𝐨𝐧𝐯𝐞𝐫𝐬𝐢𝐨𝐧 & 𝐎𝐩𝐞𝐫𝐚𝐭𝐨𝐫𝐬 Another step forward in my Python learning journey 🐍 — building strong fundamentals that are essential for data science and AI. 📚 𝐖𝐡𝐚𝐭 𝐈 𝐜𝐨𝐯𝐞𝐫𝐞𝐝: 🧩 𝐃𝐚𝐭𝐚 𝐒𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐬 • Lists, Tuples, Sets, Dictionaries • Understanding how data is stored and managed efficiently 🔄 𝐓𝐲𝐩𝐞 𝐂𝐨𝐧𝐯𝐞𝐫𝐬𝐢𝐨𝐧 & 𝐂𝐚𝐬𝐭𝐢𝐧𝐠 • Converting data types (int, float, str, bool) • Writing cleaner and more flexible code ➕ 𝐏𝐲𝐭𝐡𝐨𝐧 𝐎𝐩𝐞𝐫𝐚𝐭𝐨𝐫𝐬 • Arithmetic, Comparison, and Logical operations • Building logic behind real-world programs 💡 𝐊𝐞𝐲 𝐋𝐞𝐬𝐬𝐨𝐧: Strong fundamentals are the foundation of advanced skills. Small concepts today lead to powerful applications tomorrow. 📈 Consistency in learning is what turns basic coding into real-world problem-solving. #Python #DataScience #AI #Programming #LearningJourney #Coding #TechSkills

Day 10/60: Meet Pandas—The Data Scientist’s Best Friend! 🐼📊 Double digits! Today marks Day 10 of the #60DaysOfCode challenge with ABTalksOnAI, and I’ve officially moved into the world of DataFrames. 🚀 The Mission: 🎯 Stop typing out data manually and start importing real-world files! I used the Pandas library to pull in a CSV file and display the first 10 rows of data. The Breakthrough: 💡 Pandas takes messy data and turns it into a structured, searchable table. It’s like having Excel's power combined with Python's automation. 🦾 Why this matters for AI: 🤖 An AI is only as good as the data it's trained on. Pandas is the industry-standard tool for "Data Wrangling"—cleaning and organizing information so that Machine Learning models can actually understand it. 🛠️✨ One sixth of the way through the challenge! The journey is getting more exciting every day. 📈 #ABTalks #60DaysOfCode #Pandas #Python #DataScience #BigData #AI #MachineLearning #LearningInPublic