[Gssoc'23] Added Laptop Price Predictor | Machine Learning Project pr…

…anjay-poddar#3136 (pranjay-poddar#3254)

* adding laptop price predictor model

* Update README,md

---------

Co-authored-by: MOHIT GUPTA <[email protected]>

Machine Learning Model - Deployment/Laptop-Price-Predictor/Procfile

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+web: sh setup.sh && streamlit run app.py

Machine Learning Model - Deployment/Laptop-Price-Predictor/README.md

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+# Laptop-Price-Predictor
+
+This project aims to predict the price of laptops using machine learning techniques, specifically the XGBoost algorithm. The model is trained on a dataset containing various features of laptop, such as the model, ram, sdd, touchscreen, processor, and other relevant factors.
+
+## Dataset
+
+The dataset used for this project consists of a collection of all the laptops, each with associated features and the corresponding price. The dataset is preprocessed to handle missing values, categorical variables, and feature scaling, ensuring the data is suitable for training the XGBoost model.  
+
+Dataset link: In the dataset folder(laptop_data.csv)
+## XGBoost Algorithm
+
+XGBoost is an optimized gradient boosting algorithm that has gained popularity in machine learning competitions and has become a popular choice for predictive modeling tasks. It is known for its efficiency, accuracy, and flexibility. XGBoost combines multiple weak prediction models (decision trees) to create a strong ensemble model.
+
+## Dependencies
+
+The following dependencies are required to run the project:
+
+-streamlit==1.24.1
+-scikit-learn==1.2.1
+-xgboost==1.7.6
+-pandas==1.5.3
+-numpy==1.25.0
+
+To install the required dependencies, you can use the following command:
+
+```shell
+pip install xgboost numpy pandas scikit-learn streamlit
+```
+
+## Usage
+Clone the repository:
+```shell
+git clone https://github.com/your-username/laptop-price-predictor.git
+```
+Navigate to the project directory:
+```shell
+cd laptop-price-predictor
+```
+Install the dependencies:
+```shell
+pip install -r requirements.txt
+```
+Run the Streamlit app:
+```shell
+streamlit run app.py
+```
+
+Open your browser and go to http://localhost:8501/ to access the car price prediction app.  
+
+Or you can use the deployed project using the link: https://laptop-price-predictor--eh7dddyzs0h.streamlit.app/
+
+## Disclaimer
+The laptop price predictions provided by this project are based on a machine learning model Accuracy of 89.9% and may not always accurately reflect the real market prices. The predictions should be used for reference purposes only, and actual laptop prices can vary due to various factors.

Machine Learning Model - Deployment/Laptop-Price-Predictor/app.py

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+import streamlit as st
+import pickle
+import sklearn
+import pandas as pd
+import numpy as np
+import XGBRegressor
+
+# import the model
+pipe = pickle.load(open('pipe.pkl', 'rb'))
+df = pickle.load(open('df.pkl', 'rb'))
+
+st.title("Laptop Predictor")
+
+# brand
+company = st.selectbox('Brand',df['Company'].unique())
+
+# type of laptop
+type = st.selectbox('Type',df['TypeName'].unique())
+
+# Ram
+ram = st.selectbox('RAM(in GB)',[2,4,6,8,12,16,24,32,64])
+
+# weight
+weight = st.number_input('Weight of the Laptop')
+
+# Touchscreen
+touchscreen = st.selectbox('Touchscreen',['No','Yes'])
+
+# IPS
+ips = st.selectbox('IPS',['No','Yes'])
+
+# screen size
+screen_size = st.number_input('Screen Size')
+
+# resolution
+resolution = st.selectbox('Screen Resolution',['1920x1080','1366x768','1600x900','3840x2160','3200x1800','2880x1800','2560x1600','2560x1440','2304x1440'])
+
+#cpu
+cpu = st.selectbox('CPU', df['Cpu brand'].unique())
+
+hdd = st.selectbox('HDD(in GB)', [0,128,256,512,1024,2048])
+
+ssd = st.selectbox('SSD(in GB)', [0,8,128,256,512,1024])
+
+gpu = st.selectbox('GPU', df['Gpu brand'].unique())
+
+os = st.selectbox('OS', df['os'].unique())
+
+if st.button('Predict Price'):
+    # query
+    ppi = None
+    if touchscreen == 'Yes':
+        touchscreen = 1
+    else:
+        touchscreen = 0
+
+    if ips == 'Yes':
+        ips = 1
+    else:
+        ips = 0
+
+    X_res = int(resolution.split('x')[0])
+    Y_res = int(resolution.split('x')[1])
+    ppi = ((X_res**2) + (Y_res**2))**0.5/screen_size
+    query = np.array([company,type,ram,weight,touchscreen,ips,ppi,cpu,hdd,ssd,gpu,os])
+    query = np.array(query, dtype=object)
+
+    query = query.reshape(1,12)
+    st.title("The predicted price of this configuration is " + str(int(np.exp(pipe.predict(query)[0]))))
+
+
+