• 👋 Hi, I’m @Prathamesh-dev-code
• 👀 I’m interested in Machine Learning Projects
• 🌱 I’m currently learning more about data science
• 📫 How to reach me (www.linkedin.com/in/prathamesh-pandhare-7b8583255)

Workflow:

I've designed a step-by-step workflow for stock price prediction. It all starts with gathering historical stock price data. Then, I've put in the effort to preprocess this data, ensuring it's clean and ready for analysis. After that, I split the data into training and testing sets, a fundamental step in machine learning. With the groundwork laid, I constructed a powerful Linear Regression model using the training data. The next crucial step was evaluating the model's performance to ensure it can make accurate predictions. Finally, with a well-trained model in hand, I can confidently forecast future stock prices.

What is Linear Regression:

I harnessed the potential of Linear Regression, a foundational machine learning technique, for this project. This algorithm, rooted in supervised learning, excels at establishing connections between two continuous variables: one acting as an independent variable and the other as a dependent variable. In this context, I employed Linear Regression to predict future stock prices based on a wealth of historical data.

Importing Library:

To make this project possible, I imported essential libraries into my code. This included heavyweights like NumPy, pandas, and scikit-learn for machine learning, along with visualization tools like Matplotlib or Seaborn to help with data exploration and presentation.

Loading Dataset:

The heart of this project is the dataset. I loaded stock price data name INR=X.csv, meticulously selected to contain all the critical information needed for model training and testing.

Data Preprocessing:

I knew that data preparation is the key to success. Hence, I meticulously preprocessed the dataset. This involved cleaning up messy data, addressing any missing values, and transforming it into a format suitable for machine learning.

Model Building:

The core of this project was creating and training a Linear Regression model. I allowed the model to learn from the historical stock price data, uncovering patterns and relationships crucial for predicting future stock prices.

Model Evaluation:

Once the model was trained, I put it to the test. I used various metrics, including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared, to rigorously evaluate its predictive capabilities. I also visualized the model's predictions alongside actual stock prices, making it easier to assess its performance.