📈 Predictive Modeling for Future Stock Prices in Python: A Step-by-Step Guide
The process of building a stock price prediction model using Python.
1. Import required modules
2. Obtaining historical data on stock prices
3. Selection of features.
4. Definition of features and target variable
5. Preparing data for training
6. Separation of data into training and test sets
7. Building and training the model
8. Making forecasts
9. Trading Strategy Testing
The process of building a stock price prediction model using Python.
1. Import required modules
2. Obtaining historical data on stock prices
3. Selection of features.
4. Definition of features and target variable
5. Preparing data for training
6. Separation of data into training and test sets
7. Building and training the model
8. Making forecasts
9. Trading Strategy Testing
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Automate_the_Boring_Stuff_with_Python,_2nd_Edition_Practical_Programming.pdf
13.7 MB
One of the best books to learn python
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15 Best Project Ideas for Python : 🐍
🚀 Beginner Level:
1. Simple Calculator
2. To-Do List
3. Number Guessing Game
4. Dice Rolling Simulator
5. Word Counter
🌟 Intermediate Level:
6. Weather App
7. URL Shortener
8. Movie Recommender System
9. Chatbot
10. Image Caption Generator
🌌 Advanced Level:
11. Stock Market Analysis
12. Autonomous Drone Control
13. Music Genre Classification
14. Real-Time Object Detection
15. Natural Language Processing (NLP) Sentiment Analysis
🚀 Beginner Level:
1. Simple Calculator
2. To-Do List
3. Number Guessing Game
4. Dice Rolling Simulator
5. Word Counter
🌟 Intermediate Level:
6. Weather App
7. URL Shortener
8. Movie Recommender System
9. Chatbot
10. Image Caption Generator
🌌 Advanced Level:
11. Stock Market Analysis
12. Autonomous Drone Control
13. Music Genre Classification
14. Real-Time Object Detection
15. Natural Language Processing (NLP) Sentiment Analysis
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4 Python practical projects to do for freshers in data analytics
🧵⬇️
1️⃣ Exploratory Data Analysis (EDA) on a Public Dataset
Use a dataset from Kaggle or data.gov
Clean and preprocess the data
Perform statistical analysis and visualization
Draw insights and present findings
2️⃣ Stock Market Analysis Tool
Fetch real-time stock data using an API (e.g., yfinance)
Implement technical indicators (e.g., moving averages, RSI)
Create visualizations of stock performance
Build a simple prediction model
3️⃣ Social Media Sentiment Analysis
Collect tweets or Reddit posts using APIs
Preprocess text data
Perform sentiment analysis
Visualize sentiment trends over time
4️⃣ Customer Churn Prediction
Use a telecom or e-commerce dataset
Perform feature engineering
Build and compare multiple machine learning models
Evaluate model performance and interpret results
Hope it helps :)
🧵⬇️
1️⃣ Exploratory Data Analysis (EDA) on a Public Dataset
Use a dataset from Kaggle or data.gov
Clean and preprocess the data
Perform statistical analysis and visualization
Draw insights and present findings
2️⃣ Stock Market Analysis Tool
Fetch real-time stock data using an API (e.g., yfinance)
Implement technical indicators (e.g., moving averages, RSI)
Create visualizations of stock performance
Build a simple prediction model
3️⃣ Social Media Sentiment Analysis
Collect tweets or Reddit posts using APIs
Preprocess text data
Perform sentiment analysis
Visualize sentiment trends over time
4️⃣ Customer Churn Prediction
Use a telecom or e-commerce dataset
Perform feature engineering
Build and compare multiple machine learning models
Evaluate model performance and interpret results
Hope it helps :)
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