Laptop_price_prediction_using_ml_ppt.pptx

Laptop price prediction using machine learning ppt

1. MACHINE LEARNING MINI PROJECT LAPTOP PRICE PREDICTION.

2. Regression A regression is a statistical technique that relates a dependent variable to one or more independent (explanatory) variables. A regression model is able to show whether changes observed in the dependent variable are associated with changes in one or more of the explanatory variables. It does this by essentially fitting a best-fit line and seeing how the data is dispersed around this line.

3. LINEAR REGRESSION Linear regression is one of the easiest and most popular Machine Learning algorithms. It is a statistical method that is used for predictive analysis. Linear regression makes predictions for continuous/real or numeric variables such as sales, salary, age, product price, etc. TYPES OF LINEAR REGRESSION Simple Linear Regression: If a single independent variable is used to predict the value of a numerical dependent variable, then such a Linear Regression algorithm is called Simple Linear Regression. Multiple Linear regression: If more than one independent variable is used to predict the value of a numerical dependent variable, then such a Linear Regression algorithm is called Multiple Linear Regression.

4. PROBLEM STATEMENT We will make a project for Laptop price prediction.The problem statement is that if any user wants to buy a laptop then our application should be compatible to provide tentative price of laptop according to te user configurations

5. LIBRARIES AND TOOLS USED Numpy -  library  used for working with arrays Pandas -   library for data analysis Matplotlib - library for creating static and interactive visualizations Seaborn - data visualization library based on matplotlib . Sklearn - library that implements range of machine learning algorithms.

6. DATASET FOR LAPTOP PRICE PREDICTION

7. ATTRIBUTES OF DATASET COMPANY TYPENAME INCHES SCREEN RESOLUTION CPU RAM MEMORY GPU WEIGHT PRICE

8. DISTRIBUTION OF TARGET COLOUMN working with regressionproblem statement target coloumn distribution is important to understand The distribution of target variable is skewed and its obvious that comodities with low prices are sold and purchased more than te branded ones.

9. COMPANY COLUMN We want to understand how does brand name impact the laptop price or what is the average price of each laptop brand? Now if we plot the company relationship with price then you can observe that how price varies different brands.

10. TYPE NAME COLUMN It tells us about which type of laptop you are looking for like Gaming laptop,Workstation or Notebook.As major people prefer Notebook because it is under budget range and the same can be concluded from our data.

11. DOES THE PRICE VARY FROM SIZE [INCHES]? From the below plot we can conclude that there is a relationship but not a strong relationship between the price and size column.

12. VARIATION OF PRICE WITH RAM We can again use our barplot property to plot variation of price with ram.From the bar plot we can observe that laptops with 64 Gb ram is expensive followed by 32 Gb and so on.Here we can conclude that price will depend on the ram.

13. VARIATION OF PRICE WITH COMPANY We use scatter plot to plot the variation of price with company.By ploting the graph we can observe that Razer,Apple,Lg,Microsoft,Google,MSI laptops are expensive and others are in the budget range .

14. LABEL ENCODER Label Encoding refers to converting the labels into a numeric form so as to convert them into the machine-readable form. Machine learning algorithms can then decide in a better way how those labels must be operated

15. MULTIPLE LINEAR REGRESSION Multiple Linear Regression attempts to model the relationship between two or more features and a response by fitting a linear equation to observed data. The steps to perform multiple linear Regression are almost similar to that of simple linear Regression. The Difference Lies in the evaluation. We can use it to find out which factor has the highest impact on the predicted output and now different variables relate to each other

16. MULTIPLE LINEAR REGRESSION

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