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Used Car Price Prediction Web deployment and Key learnings

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Building a Web App for Used Car Price Prediction Using Streamlit In the previous blog, we built a machine learning model to predict used car prices based on various features such as company, car model, year, fuel type, and kilometers driven. In this post, we will take the next step and build an interactive web application using Streamlit that will allow users to input car details and get an estimated price prediction. Streamlit Setup: Streamlit is an open-source app framework in Python that enables you to create beautiful, interactive web applications with minimal effort. By using Streamlit, we can easily deploy our trained machine learning model and build a user-friendly interface for making predictions. Let's walk through the process of creating the web application. 1. Importing Required Libraries We start by importing the necessary libraries—Streamlit for the web app, Pandas for data manipulation, NumPy for numerical operations, and scikit-learn for machine learning functions. ...
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Used Car Price Predictor (EDA and Model )

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  Exploratory Data Analysis (EDA) and Model Building for Used Car Price Prediction With the dataset cleaned and preprocessed, it's time to take the next step— Exploratory Data Analysis (EDA) and model building. In this blog, we will begin by exploring the dataset to uncover valuable insights and identify relationships between key features, such as year, mileage, fuel type, and brand, and their impact on car prices. Through visualizations and statistical analysis, we'll identify trends and correlations that will guide the construction of a machine learning model. After the EDA, we'll move on to building a predictive model that can accurately estimate the price of a used car based on its attributes. Exploratory Data Analysis Let's create a boxplot to visualize the distribution of used car prices for each company in the dataset: import matplotlib . pyplot as plt import seaborn as sns plt . subplots ( figsize = ( 15 , 7 )) ax = sns . boxplot ( x = 'company' ...
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Car Price Predictor (Data Preparation)

  Predicting Used Car Prices: A Machine Learning Approach The used car market is vast and dynamic, with prices influenced by various factors such as brand, model, year, mileage, fuel type, and location. Accurately estimating the price of a used car can be a challenging task for both buyers and sellers who are looking to make informed decisions. In this blog, I walk you through the process of cleaning and preparing a dataset to predict used car prices using machine learning. By working through various data preprocessing steps—such as handling missing values, converting data types, and dealing with outliers—I ensure that the dataset is ready for Exploratory Data Analysis (EDA) . This will allow us to identify patterns and relationships that will help build an effective predictive model. Loading and Exploring the Data We start by loading the dataset and understanding its structure: import pandas as pd import numpy as np cars = pd . read_csv ( 'quikr_car.csv' ) Data Overvie...
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Banglore House Price Prediction (Deploying Realtime) and Key Learnings of the project

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  Building a Web App for House Price Prediction: Deploying the Machine Learning Model In our previous blog, we explored Exploratory Data Analysis (EDA) and built a predictive model to estimate house prices. After testing multiple regression models, XGBoost Regressor proved to be the most accurate, and we saved the trained model using pickle for future use. Now, it's time to take the next step— deploying our model as a web application ! A web app allows users to interact with the model in real-time, making predictions based on their input. In this blog, we will: Set up a web framework using Streamlit to build an interactive UI. Load the saved model and use it for predictions. Create a user-friendly interface where users can enter property details and get instant price estimates. Deploy the web app so it can be accessed by anyone online. Step-by-Step Implementation 1. Import Required Libraries First, we import the necessary libraries: import streamlit as st import pand...
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