🌟 Welcome to my Data Science internship at Cipher Technologies! 🌟

During this internship, I had the opportunity to work on various data science tasks and projects. The primary focus was on two main tasks:

1. Iris Flower Classification: I worked on building a classification model to predict the species of iris flowers based on their features. I explored different machine learning algorithms including Logistic Regression (LR), Decision Trees (DT), Support Vector Machines (SVM), k-Nearest Neighbors (KNN), and Gaussian Naive Bayes (NB).

2. Time Series Forecasting: I also delved into time series forecasting by predicting future values based on historical data. For this task, I employed the ARIMA (AutoRegressive Integrated Moving Average) model.

In the Iris Flower Classification project, I utilized several machine learning algorithms to classify iris flowers into different species based on their sepal and petal measurements. The algorithms used are as follows:

• Logistic Regression (LR)
• Decision Trees (DT)
• Support Vector Machines (SVM)
• k-Nearest Neighbors (KNN)
• Gaussian Naive Bayes (NB)

I evaluated the performance of each algorithm using accuracy metrics and visualized the results to compare their performance.

In the Time Series Forecasting project, I focused on predicting future values of a time series dataset. The ARIMA model was employed to forecast future values based on historical temperature data. I evaluated the performance of the ARIMA model using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE).

• RMSE (Root Mean Squared Error): This metric measures the average magnitude of the errors between predicted and actual values. Lower values indicate better model performance.
• MAE (Mean Absolute Error): This metric measures the average absolute differences between predicted and actual values. Lower values indicate better model performance.

1. Clone the repository:

2. Navigate to the respective project directory (IrisClassify or Time series forecast).

3. Run the Jupyter notebooks (*.ipynb) to explore the code and results.

Feel free to reach out if you have any questions or suggestions! Happy coding! 😊