📈 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
👍7
Automate_the_Boring_Stuff_with_Python,_2nd_Edition_Practical_Programming.pdf
13.7 MB
One of the best books to learn python
👍7
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
👍1
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 :)
👍6
FREE RESOURCES TO LEARN PYTHON
👇👇
Free Udacity Course to learn Python
https://imp.i115008.net/5bK93j
Data Structure and OOPS in Python Free Courses
https://bit.ly/3t1WEBt
Free Certified Python course by Freecodecamp
https://www.freecodecamp.org/learn/scientific-computing-with-python/
Free Python Course from Google
https://developers.google.com/edu/python
Free Python Tutorials from Kaggle
https://www.kaggle.com/learn/python
Python hands-on Project
https://t.iss.one/Programming_experts/23
Free Python Books Collection
https://cfm.ehu.es/ricardo/docs/python/Learning_Python.pdf
https://static.realpython.com/python-basics-sample-chapters.pdf
👨💻Websites to Practice Python
1. https://codingbat.com/python
2. https://www.hackerrank.com/
3. https://www.hackerearth.com/practice/
4. https://projecteuler.net/archives
5. https://www.codeabbey.com/index/task_list
6. https://www.pythonchallenge.com/
Beginner's guide to Python Free Book
https://t.iss.one/pythondevelopersindia/144
Official Documentation
https://docs.python.org/3/
Join @free4unow_backup for more free courses
ENJOY LEARNING 👍👍
👇👇
Free Udacity Course to learn Python
https://imp.i115008.net/5bK93j
Data Structure and OOPS in Python Free Courses
https://bit.ly/3t1WEBt
Free Certified Python course by Freecodecamp
https://www.freecodecamp.org/learn/scientific-computing-with-python/
Free Python Course from Google
https://developers.google.com/edu/python
Free Python Tutorials from Kaggle
https://www.kaggle.com/learn/python
Python hands-on Project
https://t.iss.one/Programming_experts/23
Free Python Books Collection
https://cfm.ehu.es/ricardo/docs/python/Learning_Python.pdf
https://static.realpython.com/python-basics-sample-chapters.pdf
👨💻Websites to Practice Python
1. https://codingbat.com/python
2. https://www.hackerrank.com/
3. https://www.hackerearth.com/practice/
4. https://projecteuler.net/archives
5. https://www.codeabbey.com/index/task_list
6. https://www.pythonchallenge.com/
Beginner's guide to Python Free Book
https://t.iss.one/pythondevelopersindia/144
Official Documentation
https://docs.python.org/3/
Join @free4unow_backup for more free courses
ENJOY LEARNING 👍👍
👍8
Essentials for Acing any Data Analytics Interviews-
SQL:
1. Beginner
- Fundamentals: SELECT, WHERE, ORDER BY, GROUP BY, HAVING
- Essential JOINS: INNER, LEFT, RIGHT, FULL
- Basics of database and table creation
2. Intermediate
- Aggregate functions: COUNT, SUM, AVG, MAX, MIN
- Subqueries and nested queries
- Common Table Expressions with the WITH clause
- Conditional logic in queries using CASE statements
3. Advanced
- Complex JOIN techniques: self-join, non-equi join
- Window functions: OVER, PARTITION BY, ROW_NUMBER, RANK, DENSE_RANK, lead, lag
- Query optimization through indexing
- Manipulating data: INSERT, UPDATE, DELETE
Python:
1. Basics
- Understanding syntax, variables, and data types: integers, floats, strings, booleans
- Control structures: if-else, loops (for, while)
- Core data structures: lists, dictionaries, sets, tuples
- Functions and error handling: lambda functions, try-except
- Using modules and packages
2. Pandas & Numpy
- DataFrames and Series: creation and manipulation
- Techniques: indexing, selecting, filtering
- Handling missing data with fillna and dropna
- Data aggregation: groupby, data summarizing
- Data merging techniques: merge, join, concatenate
3. Visualization
- Plotting basics with Matplotlib: line plots, bar plots, histograms
- Advanced visualization with Seaborn: scatter plots, box plots, pair plots
- Plot customization: sizes, labels, legends, colors
- Introduction to interactive visualizations with Plotly
Excel:
1. Basics
- Cell operations and basic formulas: SUMIFS, COUNTIFS, AVERAGEIFS
- Charts and introductory data visualization
- Data sorting and filtering, Conditional formatting
2. Intermediate
- Advanced formulas: V/XLOOKUP, INDEX-MATCH, complex IF scenarios
- Summarizing data with PivotTables and PivotCharts
- Tools for data validation and what-if analysis: Data Tables, Goal Seek
3. Advanced
- Utilizing array formulas and sophisticated functions
- Building a Data Model & using Power Pivot
- Advanced filtering, Slicers and Timelines in Pivot Tables
- Crafting dynamic charts and interactive dashboards
Power BI:
1. Data Modeling
- Importing data from diverse sources
- Creating and managing dataset relationships
- Data modeling essentials: star schema, snowflake schema
2. Data Transformation
- Data cleaning and transformation with Power Query
- Advanced data shaping techniques
- Implementing calculated columns and measures with DAX
3. Data Visualization and Reporting
- Developing interactive reports and dashboards
- Visualization types: bar, line, pie charts, maps
- Report publishing and sharing, scheduling data refreshes
Statistics:
Mean, Median, Mode, Standard Deviation, Variance, Probability Distributions, Hypothesis Testing, P-values, Confidence Intervals, Correlation, Simple Linear Regression, Normal Distribution, Binomial Distribution, Poisson Distribution
SQL:
1. Beginner
- Fundamentals: SELECT, WHERE, ORDER BY, GROUP BY, HAVING
- Essential JOINS: INNER, LEFT, RIGHT, FULL
- Basics of database and table creation
2. Intermediate
- Aggregate functions: COUNT, SUM, AVG, MAX, MIN
- Subqueries and nested queries
- Common Table Expressions with the WITH clause
- Conditional logic in queries using CASE statements
3. Advanced
- Complex JOIN techniques: self-join, non-equi join
- Window functions: OVER, PARTITION BY, ROW_NUMBER, RANK, DENSE_RANK, lead, lag
- Query optimization through indexing
- Manipulating data: INSERT, UPDATE, DELETE
Python:
1. Basics
- Understanding syntax, variables, and data types: integers, floats, strings, booleans
- Control structures: if-else, loops (for, while)
- Core data structures: lists, dictionaries, sets, tuples
- Functions and error handling: lambda functions, try-except
- Using modules and packages
2. Pandas & Numpy
- DataFrames and Series: creation and manipulation
- Techniques: indexing, selecting, filtering
- Handling missing data with fillna and dropna
- Data aggregation: groupby, data summarizing
- Data merging techniques: merge, join, concatenate
3. Visualization
- Plotting basics with Matplotlib: line plots, bar plots, histograms
- Advanced visualization with Seaborn: scatter plots, box plots, pair plots
- Plot customization: sizes, labels, legends, colors
- Introduction to interactive visualizations with Plotly
Excel:
1. Basics
- Cell operations and basic formulas: SUMIFS, COUNTIFS, AVERAGEIFS
- Charts and introductory data visualization
- Data sorting and filtering, Conditional formatting
2. Intermediate
- Advanced formulas: V/XLOOKUP, INDEX-MATCH, complex IF scenarios
- Summarizing data with PivotTables and PivotCharts
- Tools for data validation and what-if analysis: Data Tables, Goal Seek
3. Advanced
- Utilizing array formulas and sophisticated functions
- Building a Data Model & using Power Pivot
- Advanced filtering, Slicers and Timelines in Pivot Tables
- Crafting dynamic charts and interactive dashboards
Power BI:
1. Data Modeling
- Importing data from diverse sources
- Creating and managing dataset relationships
- Data modeling essentials: star schema, snowflake schema
2. Data Transformation
- Data cleaning and transformation with Power Query
- Advanced data shaping techniques
- Implementing calculated columns and measures with DAX
3. Data Visualization and Reporting
- Developing interactive reports and dashboards
- Visualization types: bar, line, pie charts, maps
- Report publishing and sharing, scheduling data refreshes
Statistics:
Mean, Median, Mode, Standard Deviation, Variance, Probability Distributions, Hypothesis Testing, P-values, Confidence Intervals, Correlation, Simple Linear Regression, Normal Distribution, Binomial Distribution, Poisson Distribution
👍4