Let’s create a project that can predict the stock price of Maruti Company. 1. Import Libraries. 2. Read the CSV file and Take input from the user that he wants to predict. 3. Make functions to calculate RSME and Accuracy Score. 4. Apply the KNN, Random Forest, Multiple Linear Regression, and Logistic Regression. 5. Find out the best algorithm among these based on RSME and Accuracy Score.
Let’s create a project that can detect the lane line of the road in an image. 1. Load a file and read an image. 2. Convert original image to grayscale image. 3. Apply Gaussian Blur to reduce the noise in the grayscale image. 4. Apply the Canny method. 5. Make a function to find a region of interest—the area where we want to see the lanes. Then, apply the bitwise AND operation to select the region of interest through a transform to find straight lines in the image. 6. Do some optimization to refine the detection of lanes.
You have to create three tables- Salesman, Customer, and Orders and have to perform these queries: 1. Write a query to find customers with their names, salesmen with their names, and city that lives in the same city. 2. Find the names of all customers along with the salesmen who work for them 3. Display all those orders by the customers not located in the same cities where their salesmen live. 4. Find out each order number followed by the name of the customers who made the order. 5. Sort out the customer and their grade who made an order. Each customer must have a grade and serve by at least a salesman who belongs to a city. 6. Write a query which produces customers with their: name, city, commission, and salesman, who served by a salesman and salesman works at a rate of the commission within 12% to 14%, respectively. 7. Write the query which Produces all orders with the order number, customer name, commission rate & earned commission amount for the customers who carry their grade is 200 or more and are served by an existing salesman. 8. Display all customers with orders on October 5, 2012.