Developing an amazing product is one of the hardest challenges that a company can overcome and one of the biggest predictors of success. But strong customer relations is what will truly determine the success of a business.

At its core, customer relationship management (CRM) is all of the activities, strategies and technologies that companies use to manage their interactions with their current and potential customers. A saying frequently heard and said in many businesses is "customer is king."

Today's consumers have more industry influence than they've ever had in the past, allowing them to focus on more than just the product that you're selling them. Now, consumers are interested in what you're selling them, how you're selling it, and what happens after you've sold it to them.

To meet the demands for greater customer experiences, companies must focus on managing their customer relationships. Strong customer relations are essential for building customer loyalty and retaining current customers. This means understanding what is required to ensure top-quality customer relationships and create great experiences with your customers.

Positive customer relations can result in an array of benefits for your company, including more potential leads and higher customer retention rates. To narrow it down, here are several top benefits that positive customer relations can provide for your company.

• Customer Retention
• Customer Loyalty
• Customer Satisfaction
• Customer Feedback

• Collected the data and organized it to form a meaningful dataset.
• Checked for null values and took care of it.
• Observed the data to form meaningful insights!
  
  
• Did Exploratory Data Analysis on the dataset.
• Used correlations to form a heatmap.

• Visualizations were made by using Matplotlib and Seaborn Libraries..!

• Made Binary Classifications Using :
• Label Encoder
• Standard Scaler
  To fit and transform Numerical and Categorical Column values.

Dimensionality reduction is way to reduce the complexity of a model and avoid overfitting.

Principal Component Analysis (PCA) algorithm used to compress a dataset onto a lower-dimensional feature subspace with the goal of maintaining most of the relevant information.

• Fitted the scaled data
• Transformed it to meaningful columns for better analysis.

Visualized the end predicted results using Clusters

• Visualizing it in reduced dimension.

• Fitted the model
• Plotted the end result.

by distributing them into groups above and below

• 0: High spending and average income
• 1: High consumption and high income
• 2: Low spending and low income
• 3: High spending and low income