Salesforce is a CRM of today’s world and can be implemented easily by several organizations to manage their business operations. Salesforce offers a number of enterprise management tools that can help managers in managing applications. PYTHON TO GET SALESFORCE DATA? Salesforce focuses on development of programs in such a way that systems can access data and use it to learn for themselves. Salesforce can be implemented in different programming languages and Python leads the lot by being the most widely used amongst them. All the tech giants are investing a major amount of their resources in these fields and are looking for fresh minds for the same. In order to obtain Salesforce using python, you should have knowledge of some common terms used in salesforce machine learning, which are given below: Algorithm Model Feature Label Pre-Processing WHY PYTHON? The Salesforce migration is a complicated task that involves moving data from a legacy system into a Salesforce software. Clients provide Salesforce teams with batches of client access that need to be loaded into Salesforce. Before the data can be loaded into Salesforce, teams must analyze, clean, and prep the important information. This process is known as data transformation. This transformations can be difficult, time-consuming tasks to perform manually in Excel. Hence, you can use Python to automate your data transformations before you load the data into Salesforce. Below are some of the major benefits you’ll experience when using Python over Excel. Python is free to use Python handles does a great job handling large sets of data, while Excel tends to crash Python runs operations MUCH faster than Excel Python can read larger files than Excel Python formats the data as expected METHOD OF OBTAINING SALESFORCE DATA USING PYTHON Data frame Creation Data capturing uses N-dimensional arrays of NUMPY and PANDAS data frame. Data frames are like excel sheets in which we can define indexes or names to rows and columns. Data-preprocessing In order to convert textual data into numerical data it’s preferable to use OneHotEncoder or LabelEncoder but it completely depends on the developer’s choice. Splitting of data frame Data frames as we specify above can be simply broken into input and output labels. Recursive Feature Elimination Recursive feature elimination is a method of recursively removing features and creating a model on the specified number of characteristics. K-Fold Cross-Validation and model fit Once we finish all of that we go on to K-fold validation. K-fold cross-validation is a resampling method used to assess a model on a limited set of data. Any kind of K-fold validation can be used but we favor Stratified K-fold. Model Persistence Lastly, the model is persevered using the pickle library for future forecasts. Conclusion The above steps will help the newcomers boost their initial machine learning concepts in salesforce and will act as a jump start for your efforts in learning machine learning with python. Of course, there are options open for development in contradiction to Python salesforce, the common ones being, of course, Java, Scala, and Go. But the business and libraries have a monopoly of salesforce with python. If you are a person who wants to try different things the options are open. But if you want to save the pain and concentrate only on the product then python salesforce is always your option.