KNN Predict a price range of mobile phones, indicating how high the price is, using K-Nearest Neighbors algorithm. Steps: 1.Loading the dataset Load the dataset into a pandas dataframe. 2.Data-Preprocessing 2.1.Handling missing values Check if there are any missing values in the dataset. If there are any missing values, handle them using techniques like imputation. 2.2.Handling outliers Check if there are any outliers in the dataset. 2.3.Feature Scaling using Standardscalar Scale the features using StandardScaler. 2.4.Checking for imbalaned dataset Check if the dataset is imbalanced. If it is imbalanced, handle it using techniques like oversampling or undersampling. 3.EDA Perform exploratory data analysis to get insights about the dataset. 4.Splitting the dataset into train and test Split the dataset into training and testing sets. 5.Building the model 5.1.Predicting Train a K-Nearest Neighbors model on the training set and use it to predict the price range on the testing set. 5.2.Performance metrics Evaluate the performance of the model using performance metrics like accuracy, precision, recall, and F1-score. 5.3.Chossing optimum k value Choose the optimum value of k by plotting the error rate against different values of k and selecting the value of k that gives the lowest error rate.