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Data Science Bundle: 180 Hands-On Projects - Course 3 of 3

Name: Data Science Bundle: 180 Hands-On Projects - Course 3 of 3
Rating: 4.4 (46 reviews)

Build & Deploy 180 Projects - Data Science, Machine Learning, Deep Learning (Python, Flask, Django, AWS, Azure Cloud)

Created byPianalytix • 75,000+ Students Worldwide

Last updated 10/2025

English

What you'll learn

Equip yourself with skills that are in high demand, increasing your chances of getting hired as a Data Scientist.
Apply Machine Learning to real-world scenarios, making you job-ready
Get exposure to Deep Learning, Transfer Learning, and Neural Networks, expanding your skill set.
Master the essentials of Machine Learning using Python, the go-to language for Data Science.
Learn to build robust Machine Learning models that can withstand real-world uncertainties.
Acquire techniques to fine-tune and improve your Machine Learning models for better performance.
Develop a strong intuition for choosing the right Machine Learning models for different tasks.
Empower yourself to conduct powerful data analyses that can drive decision-making processes.

Course content

61 sections • 422 lectures • 53h 34m total length

Introduction to the course0:36
Introduce the data science bundle, featuring 180 hands-on projects, in course 3 of 3 for learners new to data science.
Outline of the course0:40
Outline the course structure for course 3 of the data science bundle, highlighting the 180 hands-on projects included in this final module.
Course Bonuses: Cheat Sheets, Downloads, Mind maps, Guides.0:47

Introduction2:40
Machine Learning model building part17:20
Build a machine learning model in Colab, importing numpy, pandas, seaborn, and matplotlib, then prepare data, convert strings to numbers, and drop null rows to predict selling price.
Machine Learning model building part211:53
Machine Learning model building part313:40
Utilize pandas to inspect car data, identify outliers, and encode categoricals with mapping and get_dummies, then train a random forest regressor and save the pickle model for a Django app.
Creating Django Application part112:18
Creating Django Application part28:21
Construct a Django web app that collects form data via POST, loads a pickled model, predicts car selling price, and displays the inference on the front end.
Deploying on Heroku8:36
Download the project files0:29

Introduction3:07
Predict the number of affairs using a random forest regressor with feature engineering and PCA. Mount the model in a Django web app and deploy on Heroku via GitHub.
Intoductory Machine Learning model building12:27
Develop a machine learning model to predict affairs from a prepared dataset, using numpy, pandas, seaborn, and matplotlib in colab, performing feature selection and exploratory analysis with correlation heatmaps.
Feature Building and Selection14:16
Build and select features from education and affairs data using scatter and box plots, encode categoricals with get_dummies, apply PCA, and train a random forest regressor.
Model Building5:40
Explore building a predictive model with PCA for dimensionality reduction, train-test split, and a 300-tree random forest, plus saving the model with pickle for a Django web app.
Django Application Introduction7:07
Set up a Django web app by understanding Django as a Python-based framework, install dependencies from requirements.txt, run migrations to create tables, and launch the development server.
Django Application building15:13
Deploying on Heroku6:35
Download the project files0:27

Introduction2:57
Predict mushroom edibility (edible or poisonous) using a random forest model on 23-species dataset, encode categorical data with label encoding, build a Django app, and deploy to Heroku via GitHub.
Importing libraries and Understanding data16:48
Building the model9:57
Explore correlational graphs and seaborn heatmaps to assess correlation, apply PCA to reduce dimensions, train a random forest classifier, and save the model with pickle.
Building Django Application15:51
Delpoying on Heroku7:59
Deploy a Django web app to Heroku by configuring runtime and proc file, installing dependencies from requirements.txt, and connecting GitHub for automated deployment.
Download the project files0:23

Introduction - Google Play App Rating Prediction2:46
Introduction to libraries and dataset14:56
Preprocessing the data10:01
Explore preprocessing of a large dataset by encoding categorical features, converting strings to numbers, handling varies values, and extracting date fields and Android version data to prepare for model training.
building the model7:13
Django Application14:57
Build a Django web application using the model-template-views architecture, set up the project, run migrations and server, and manage data flow from a form to a prediction model with templates.
Deploying to Heroku7:17
Download the project files0:23

Introduction - Build Medical Cost Predictions Django App2:34
Introduction and handling the data17:03
Mount the drive folder onto the Colab notebook to access the csv data, then use numpy, pandas, seaborn, and matplotlib to build a machine learning model predicting medical costs.
Building the model6:17
Encode categorical data with a label encoder, analyze correlations with a heat map, then train a random forest regressor, evaluate on train/test data, and save the model for Django deployment.
Django Application13:25
learn to build a Django web application using the model-template-views architecture, set up a project and environment, run migrations, and connect templates and views to handle forms and data flow.
Heroku Deployment6:36
Deploy a Django web app on Heroku by configuring runtime.txt and proc file for Python 3.7.10, applying the Python buildpack, linking GitHub, and running collectstatic before releasing.
Download the project files0:22

Introduction3:00
Understanding the data14:00
Build a machine learning model to predict phishing sites by loading a 10,000-point dataset in Colab, cleaning data, and exploring features like URL length, https presence, hashes, and IP addresses.
Feature Selection and model building9:18
Django Application15:25
Learn to build a Django web application with Python-based model template views, connecting templates, models, views, and a database while integrating a phishing detection machine learning model.
Deploying on Heroku7:59
Download the project files0:23

Introduction3:46
This data science project merges machine learning and web development to predict clothing quality using a random forest model, mounted in a Django app and deployed on Heroku via GitHub.
Understanding the data10:17
Mount the drive in Colab and load libraries such as NumPy, pandas, Seaborn, and Matplotlib to prepare a cloth quality dataset, handle missing values, and drop uninformative columns.
Cleaning the data15:05
Explore handling categorical data with value counts and label encoding, and cleaning data through deep copies, type conversions, and regex-based height extraction to improve model performance.
Building the model10:09
Implementing Django Application13:30
Learn to implement a django web application by setting up a project, configuring settings and apps, building templates and views, handling forms, and deploying with runserver.
Deploying to heroku7:39
Deploy your web app on Heroku by configuring runtime.txt and a proc file, selecting the Python buildpack, connecting GitHub, and performing a manual deploy from master for Django.
Download the project files0:22

Requirements

Basic knowledge of machine learning

Description

Enroll in this course for an immersive learning experience with compelling benefits . You'll gain hands-on experience in practical machine learning and data-driven projects . Develop proficiency in Python, Flask, Django, and cloud deployment on platforms like Heroku, AWS, Azure, GCP, and Streamlit .

This course guides you through the entire project lifecycle, from ideation to deployment, with a focus on real-world applications . It bridges the gap between data analytics and business strategy, making it suitable for both newcomers and seasoned practitioners . With 60 diverse projects, you can build a robust portfolio at your own pace .

Don't miss this opportunity to advance your data science career and make a real impact in today's data-rich world . Enroll now before the offer expires and transform your future .

In This Course, We Are Going To Work On 60 Real World Projects Listed Below:

Data Science Projects:

Project-1: CarPricer: Fueling Car Prices - Build Car Prices Prediction App on Heroku

Project-2: LoveCounter: Counting Affairs - Build Affair Count Django App on Heroku

Project-3: ShroomSense: Unveiling Fungal Delights - Build Shrooming Predictions App on Heroku

Project-4: PlayRater: Play Store Insights - Google Play App Rating Prediction on Heroku

Project-5: BankGuru: Banking on Customer Predictions - Build Bank Customers Predictions Django App on Heroku

Project-6: ArtSculptor: Sculpting Artistic Insights - Build Artist Sculpture Cost Prediction Django App on Heroku

Project-7: MediCost: Healing Insights - Build Medical Cost Predictions Django App on Heroku

Project-8: PhishGuard: Safeguarding the Web - Phishing Webpages Classification Django App on Heroku

Project-9: FashionFit: Fit for Style - Clothing Fit-Size Predictions Django App on Heroku

Project-10: TextSim: Unveiling Textual Connections - Build Similarity In-Text Django App on Heroku

Project-11: ForgeryFinder: Unmasking Pan Card Tampering with AI - Deploy On Heroku

Project-12: BreedRover: Fetching Dog Breeds with a Flask Twist

Project-13: AquaMark: Immortalizing Images with Watermark Wizardry - Deploy On Heroku

Project-14: SignSense: Navigating the Road with Traffic Sign Detection

Project-15: TextXtract: Unlocking Secrets Hidden in Images

Project-16: PlantWhisperer: Decoding Nature's Language for Plant Disease Prediction

Project-17: AutoTrack: Counting Cars and Unleashing Traffic Insights with Flask

Project-18: FaceSwap Pro: Transform Faces and Dive into a World of Fun

Project-19: FeatheredForecast: Predicting Bird Species with Flask Feathers

Project-20: VisualIntel: Exploring Visual Intelligence with Intel Image Classification

Project-21: HeartBeatHero: Defending Hearts with Eval ML - Heart Attack Risk Prediction

Project-22: FraudGuardian: Shielding Finances with Pycaret - Credit Card Fraud Detection

Project-23: SkyHighForecaster: Soaring through Fare Predictions - Flight Fare Prediction

Project-24: FuelProphet: Fueling Future Insights - Petrol Price Forecasting

Project-25: ChurnSavior: Safeguarding Customer Loyalty - Bank Customer Churn Prediction

Project-26: AirQInsight: Breathing Easy with TPOT - Air Quality Index Predictor

Project-27: RainMaster: Unveiling Precipitation Patterns - Rain Prediction using ML models & PyCaret

Project-28: PizzaCraver: Predicting Pizza Prices - Pizza Price Prediction using ML and EVALML

Project-29: IPLOracle: Unlocking Cricket Magic - IPL Cricket Score Prediction using TPOT

Project-30: BikeRider: Pedaling through Rentals - Predicting Bike Rentals Count using ML and H2O Auto ML

Project-31: ConcreteWizard: Building Strong Foundations - Concrete Compressive Strength Prediction using Auto Keras

Project-32: HomePriceWhiz: Navigating the Housing Market - Bangalore House Price Prediction using Auto SK Learn

Project-33: LifeSaver: Predicting Hospital Outcomes - Hospital Mortality Prediction using PyCaret

Project-34: CareerPro: Elevating Professional Paths - Employee Evaluation for Promotion using ML and Eval Auto ML

Project-35: HydraH2O: Quenching the Thirst for Drinking Water Potability - Drinking Water Potability Prediction using ML and H2O Auto ML

Project-36: GameQuest: Unlocking the World of Video Game Sales Analysis

Project-37: TicTacToEvolved: A Strategic Battle of Wits - Build Tic Tac Toe Game

Project-38: PassGenie: Creating Secure Passwords - Random Password Generator Website using Django

Project-39: PortfolioPro: Showcasing Your Skills - Building Personal Portfolio Website using Django

Project-40: TodoTracker: Organizing Tasks Together - Todo List Website For Multiple Users

Project-41: CryptoPlanner: Riding the Waves of Crypto - Crypto Coin Planner GUI Application

Project-42: TweetBot: Your Personal Twitter Companion - Your Own Twitter Bot - Python, Request, API, Deployment, Tweepy

Project-43: DictBuilder: Crafting a Personal Dictionary - Create A Python Dictionary using Python, Tkinter, JSON

Project-44: EggCatcher: A Fun Game of Precision - Egg-Catcher Game using Python

Project-45: RoutineTracker: Keeping Your Day on Track - Personal Routine Tracker Application using Python

Project-46: ScreenPet: Unleashing the Pet on Your Screen - Building Screen-Pet using Tkinter & Canvas

Project-47: CaterpillarGame: A Journey of Transformation - Building Caterpillar Game using Turtle and Python

Project-48: HangmanMaster: Cracking the Word Code - Building Hangman Game using Python

Project-49: SmartCalc: Math Made Easy - Developing our own Smart Calculator using Python and Tkinter

Project-50: SecretSteganography: Hiding Messages in Images - Image-based steganography using Python and pillows

Power BI Projects:

Project-51: Patient Summary Dashboard: Medical Records

Project-52: Global Super Store Sales Data Analysis

Project-53: Boston Housing Dataset Dashboard: Real Estate

Project-54: Crime in Los Angeles: Yearly City Analysis

Project-55: IMDB Movie Dataset Dashboard: Movie Comparison

Project-56: Hotel Reservation Dashboard: Global Hotel Business

Project-57: Toy Sales Data Analysis: Practice Dataset

Project-58: Netflix Stock Price Dashboard: Business Analysis

Project-59: Personal Finance Management Dashboard: Financial Insights

Project-60: A Deep Dive into Bank Customer Churn with Power BI

Tips: Create A 60 Days Study Plan , Spend 1-2hrs Per Day, Build 60 Projects In 60 Days .

The Only Course You Need To Become A Data Scientist, Get Hired And Start A New Career

Note: This Course Is Worth Of Your Time And Money, Enroll Now Before Offer Expires .

Who this course is for:

Beginners in data science

Data Science Bundle: 180 Hands-On Projects - Course 3 of 3

What you'll learn

Explore related topics

Course content

Introduction to the course3 lectures • 2min

Project-1: CarPricer: Fueling Car Prices - Build Car Prices Prediction App8 lectures • 1hr 5min

Project-2: LoveCounter: Counting Affairs - Build Affair Count Django App8 lectures • 1hr 5min

Project-3: ShroomSense: Unveiling Fungal Delights - Build Shrooming Predictions6 lectures • 54min

Project-4: PlayRater: Play Store Insights - Google Play App Rating Prediction7 lectures • 58min

Project-5: BankGuru: Banking on Customer Predictions6 lectures • 53min

Project-6: ArtSculptor: Build Artist Sculpture Cost Prediction Django App6 lectures • 58min

Project-7: MediCost: Healing Insights - Build Medical Cost Predictions Django6 lectures • 46min

Project-8: PhishGuard: Safeguarding the Web - Phishing Webpages Classification6 lectures • 50min

Project-9: FashionFit: Fit for Style - Clothing Fit-Size Predictions Django App7 lectures • 1hr 1min

Requirements

Description

Who this course is for: