Description: This is for learning, studying, researching, and analyzing stock in deep learning (DL) and machine learning (ML). Predicting Stock with Machine Learning or Deep Learning with different types of algorithm. Experimenting in stock data and see how it works and why it works or why it does not works that way. Using different types of stock strategies in machine learning or deep learning. Using Technical Analysis or Fundamental Analysis in machine learning or deep learning to predict the future stock price.

1. Categorical variable(Qualitative): Label data or distinct groups.
   Example: location, gender, material type, payment, highest level of education
2. Discrete variable (Class Data): Numerica variables but the data is countable number of values between any two values.
   Example: customer complaints or number of flaws or defects, Children per Household, age (number of years)
3. Continuous variable (Quantitative): Numeric variables that have an infinite number of values between any two values. Example: length of a part or the date and time a payment is received, running distance, age (infinitly accurate and use an infinite number of decimal places)

1. For 'Quantitative data' is used with all three centre measures (mean, median and mode) and all spread measures.
2. For 'Class data' is used with median and mode.
3. For 'Qualitative data' is for only with mode.

1. Classification (predict label)
2. Regression (predict values)

Step 1 through step 8 is a reviews in python.
After step 8, everything you need to know that is relate to data analysis, data engineering, data science, machine learning, and deep learning.

1. Simple Linear Regression Model
2. Logistic Regression
3. Lasso Regression
4. Support Vector Machines
5. Polynomial Regression
6. Stepwise Regression
7. Ridge Regression
8. Multivariate Regression Algorithm
9. Multiple Regression Algorithm
10. K Means Clustering Algorithm
11. Naïve Bayes Classifier Algorithm
12. Random Forests
13. Decision Trees
14. Nearest Neighbours
15. Lasso Regression
16. ElasticNet Regression
17. Reinforcement Learning
18. Artificial Intelligence
19. MultiModal Network
20. Biologic Intelligence

Algorithms is a process and set of instructions to solve a class of problems. In addition, algorithms perform a computation such as calculations, data processing, automated reasoning, and other tasks.

Python 3.5+
Jupyter Notebook Python 3

🔻 Do not use this code for investing or trading in the stock market. However, if you are interest in the stock market, you should read 📚 books that relate to stock market, investment, or finance. On the other hand, if you into quant or machine learning, read books about 📘 machine trading, algorithmic trading, and quantitative trading. You should read 📗 about Machine Learning and Deep Learning to understand the concept, theory, and the mathematics. On the other hand, you should read academic paper and do research online about machine learning and deep learning on 💻