I have data set of client profile and mutual funds , now the problem is there is huge numbers of different mutual funds available and based on history I can see for certain type of client profile more than 20 different funds have been suggested ?Now I have no idea which algorithm should I use to come up with most recommended fund for certain client profile ?  
Data not consistent
machine learning;predictive modeling;recommender system
I assume you want to build a Recommendation Engine which will recommend mutual funds to the clients based on their profile. Recommender System is a special class of Machine Learning and make use of Supervised/Unsupervised Machine Learning techniques. According to Wikipedia, A recommender system or a recommendation system (sometimes replacing  system with a synonym such as platform or engine) is a subclass of  information filtering system that seeks to predict the rating or  preference that a user would give to an item.So essentially, the item here in your problem statement is the mutual fund of which you need to predict the preference a client would give. Higher rating/preference of a Mutual Fund, let's call it X for a client A will mean X should be recommended to A. Now, how do you give rating/preference to a list of items? Recommender Systems uses a number of approaches listed below:Popularity Based ApproachContent Based ApproachCollaborative Filtering Based Approach which composes of Nearest Neighbor and Matrix FactorizationHybrid ApproachPopularity Based Approach - Popularity Based Approach will recommend items, in your case, mutual funds, based on the total count of purchases. It will rank the mutual fund with maximum purchase in the past at the top. The item at the top will be recommended to every user/client. Advantages of Popularity Based Approach is that it is very easy toimplement. The approach is used to list out the most popular items ine-commerce websites, online news (Google News). It can use context (for example, most popular products at this time of day). It can use user and item features (for example, most popular products with feature F1, most popular products among user with feature UF1). Disadvantage of Popularity Based Approach: No personalizedrecommendation. Every one is recommended the same item.Content Based Approach - In Content Based Approach, features of both the item as well as the user/client is used to offer personalized recommendation. According to Wikipedia,Content-based filtering methods are based on a description of the item  and a profile of the users preferences.  In a content-based  recommender system, keywords are used to describe the items and a user  profile is built to indicate the type of item this user likes. In  other words, these algorithms try to recommend items that are similar  to those that a user liked in the past.So, let's say if one of the client with features UF1, UF2 purchased a mutual fund X with a set of features F1, F2 and F3 in the past. New mutual fund products, lets say Y and Z will only be recommended to this client if their features matches with that of F1, F2 and F3. It is also called Classification Based Approach because you are feeding in list of features of products and user into a classifier to predict what item should be recommended. To build a Content Based Approach model, you can use Machine Learning models like Naive Bayes Classifier or a Decision Tree. Advantage of Content Based Approach over Popularity Based Approach isthat it gives personalized recommendation. Disadvantage: It is not scalable. It is difficult to collect highquality information on user and product.Collaborative Filtering Approach - According to Wikipedia,Collaborative filtering methods are based on collecting and analyzing  a large amount of information on users behaviors, activities or  preferences and predicting what users will like based on their  similarity to other users.Without going into technical jargons, Collaborative Filtering Approach is of two types -- User-based Collaborative Filtering & Item-based Collaborative Filtering. In User-based Collaborative Filtering, let's say user/client A bought two items X and Y and user/client B bought the same items X and Y. So these two users are similar to each other based on their purchase history. Now if the user/client B purchases a third item Z, Z will also be recommended to user/client A.In Item-based Collaborative Filtering, let's say a user/client A bought items W, Y and Z. User/client B bought W and Y. User/client C bought only Y. What will be recommended to user/client C? Answer is item W because item W was co-purchased with item Y by user/client A and B. These comes under Nearest Neighbor techniques of Collaborative Filtering because we are trying to classify a user or an item by determining its nearest neighbor. An algorithm known as kNN (k-nearest Neighbor) is used to do the task.There is one another technique known as Matrix Factorization as well. Hybrid Based Approach - Hybrid Based Approach, as name says, is a combination of Content Based Approach and Collaborative Filtering Approach. It is used largely used by Netflix and Amazon to give best recommendations. 