An automobile company has plans to enter new markets with their existing products (P1, P2, P3, P4 and P5). After intensive market research, they’ve deduced that the behavior of new market is similar to their existing market.
In their existing market, the sales team has classified all customers into 4 segments (A, B, C, D ). Then, they performed segmented outreach and communication for different segment of customers. This strategy has work exceptionally well for them. They plan to use the same strategy on new markets and have identified 2627 new potential customers. You are required to help the manager to predict the right group of the new customers.
- Data exploration we have a list of features that: . ID, . Gender, . Ever_Married, . Age, . Graduated, . Profession, . Work_Experience, . Spending_Score, . Family_Size
we need to predict the target group of the new customers: . Segmentation
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Data Clearing I handeled the nan values, duplicated values, handeling the outlier values by using the interquartile method
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Data preprocessing Handling Imbalanced Data, converting the objects features into numerical values by using one hot encoder and lable encoder, applying the scaling => minmax scaler algorithm, features selection to select the important features from dataset
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Applying ML algorithms
- I used the Logistic regression algorithm,
- SVM algorithm
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showing the accuracy of the each algorithm