Prediction systems and methods are provided. The system obtains a first social media data pertaining to a first set of users, filters the first social media data to obtain a filtered social media data, generates a word embedding matrix including co-occurrence words each represented as a vector having a context, aggregates vectors pertaining each social data to obtain a first set of vectors, and trains machine learning technique(s) (MLTs) using the first set of vectors and context of the first set of vectors. The system further obtains a second social media data pertaining to a second set of users, and performs filtering, word embedding matrix generation, and aggregation operations to obtain a second set of vectors, and further applies the trained MLTs on the second set of vectors and context associated with the second set of vectors to predict age and gender of the second set of users.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A processor implemented method, comprising: (i) obtaining, using one or more hardware processors, a first social media data from one or more sources, said first social media data pertains to a first set of users; (ii) filtering said first social media data by identifying one or more stop words, and one or more expressions to obtain a first filtered social media data; (iii) generating a word embedding matrix comprising a first set of co-occurrence words from said first filtered social media data, each co-occurrence word from said first set of co-occurrence words is represented as a vector comprising context; (iv) aggregating one or more vectors pertaining to each social data submitted by each user to obtain a first set of vectors for said first set of users; and (v) training one or more machine learning techniques using said first set of vectors and context associated with said first set of vectors to obtain one or more trained machine learning techniques. 2. The processor implemented method of claim 1, further comprising: obtaining a second social media data from one or more sources, wherein said second social media data pertains to a second set of users; repeating the steps of (ii) till (iv) to obtain a second set of vectors for said second set of users based on said second social media data; and applying said one or more trained machine learning techniques (MLTs) on said second set of vectors and context associated with each of said second set of vectors. 3. The processor implemented method of claim 2, further comprising: predicting an age and a gender of each user from said second set of users upon said one or more machine learning techniques applied on said second set of vectors and said context associated with each of said second set of vectors, wherein each of said predicted age and said predicted gender are associated with a probability score. 4. The processor implemented method of claim 3, wherein applying said one or more trained MLTs includes selecting at least a subset of said one or more trained machine learning techniques based on a training level of the MLTs. 5. The processor implemented method of claim 4, wherein said at least a subset of said one or more trained machine learning techniques is selected based on a weight assigned to said one or more machine learning techniques during training. 6. A system comprising: a memory storing instructions; one or more communication interfaces; and one or more hardware processors communicatively coupled to said memory using said one or more communication interfaces, wherein said one or more hardware processors are configured by said instructions to: (i) obtain a first social media data from one or more sources, said social media data pertains to a first set of users; (ii) filter said first social media data by identifying one or more stop words, and one or more expressions to obtain a first filtered social media data; (iii) generate a word embedding matrix comprising a first set of co-occurrence words from said first filtered social media data, each co-occurrence word from said first set of co-occurrence words is represented as a vector comprising context; (iv) aggregate one or more vectors pertaining each social data submitted by each user to obtain a first set of vectors for said first set of users; and (v) train one or more machine learning techniques using said first set of vectors and context associated with said first set of vectors to obtain one or more trained machine learning techniques. 7. The system of claim 6, wherein said one or more hardware processors are further configured to obtain a second social media data from one or more sources, wherein said second social media data pertains to a second set of users, repeat the steps (ii) till (iv) to obtain a second set of vectors for said second set of users based on said second social media data, and apply said one or more machine learning techniques (MLTs) on said second set of vectors and context associated with each of said second set of vectors. 8. The system of claim 7, wherein said one or more hardware processors are further configured to predict an age and a gender of each user from said second set of users upon said one or more machine learning techniques applied on said second set of vectors and said context associated with each of said second set of vectors, wherein each of said predicted age and said predicted gender are associated with a probability score. 9. The system of claim 8, wherein said one or more hardware processors are configured to select at least a subset of said one or more machine learning techniques based on a training level of the MLTs. 10. The system of claim 9, wherein said at least a subset of said one or more machine learning techniques is selected based on a weight assigned to said one or more machine learning techniques during training. 11. One or more non-transitory machine readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors causes: (i) obtaining, using the one or more hardware processors, a first social media data from one or more sources, said first social media data pertains to a first set of users; (ii) filtering said first social media data by identifying one or more stop words, and one or more expressions to obtain a first filtered social media data; (iii) generating a word embedding matrix comprising a first set of co-occurrence words from said first filtered social media data, each co-occurrence word from said first set of co-occurrence words is represented as a vector comprising context; (iv) aggregating one or more vectors pertaining to each social data submitted by each user to obtain a first set of vectors for said first set of users; and (v) training one or more machine learning techniques using said first set of vectors and context associated with said first set of vectors to obtain one or more trained machine learning techniques. 12. The one or more non-transitory machine readable information storage mediums of claim 11, wherein the one or more instructions which when executed by the one or more hardware processors further cause: obtaining a second social media data from one or more sources, wherein said second social media data pertains to a second set of users; repeating the steps of (ii) till (iv) to obtain a second set of vectors for said second set of users based on said second social media data; and applying said one or more trained machine learning techniques (MLTs) on said second set of vectors and context associated with each of said second set of vectors. 13. The one or more non-transitory machine readable information storage mediums of claim 12, wherein the one or more instructions which when executed by the one or more hardware processors further cause: predicting an age and a gender of each user from said second set of users upon said one or more machine learning techniques applied on said second set of vectors and said context associated with each of said second set of vectors, wherein each of said predicted age and said predicted gender are associated with a probability score. 14. The one or more non-transitory machine readable information storage mediums of claim 13, wherein applying said one or more trained MLTs includes selecting at least a subset of said one or more trained machine learning techniques based on a training level of the MLTs. 15. The one or more non-transitory machine readable information storage mediums of claim 14, wherein said at least a subset of said one or more trained machine learning techniques is selected based on a weight assigned to said one or more machine learning techniques during training.