The problem LINGUALYTICS- The Age and Gender Predictor solves

By using Natural Language Processing(NLP) techniques, we can detect the age and gender of individuals based on textual data. This addresses the following challenges of automating the process of demographic identification, which can be time-consuming and prone to errors when done manually.The project is a solution for:

Targeted marketing & Advertising : It enables marketers to better understand their target audience, personalize marketing messages, make data-driven decisions, tailor their campaigns to resonate with specific age and gender demographics and improve the effectiveness of their marketing strategies, customer engagement, and drive better business outcomes.

Social Media Analytics : It helps the social media analysts to gain deeper insights into the demographics of their audience, identify trends, and tailor their content strategies to better engage with specific age and gender segments.

Personalized Healthcare Recommendations : The project supports healthcare providers in delivering more personalized and effective care, advancing medical research, and ultimately improving patient outcomes.

Enhanced Customer Services : It offers a more efficient way to extract demographic insights from customer interactions, feedback, and support tickets. By utilizing this technology, businesses can enhance customer satisfaction, increase loyalty, and drive better business outcomes by delivering more targeted and personalized customer service experiences.

Chatbot Optimization & Content Moderation : This enables chatbots to personalize responses, tailor recommendations, and adapt their conversational style based on the demographic profile of the user to improve user satisfaction, increase engagement, and ultimately enhance the overall user experience in interactions with chatbots.

Security, Law & Justice : It aids in the identification and profiling of suspects, witnesses, and victims and support security agencies in conducting more thorough and accurate investigations.

Challenges we ran into

During the making of an NLP project which can detect the age and gender of individuals from their messages, we faced several challenges. Some of the key challenges include:

Data Quality: Ensuring that the training data is of high quality and accurately labeled is crucial for the success of the model. Noisy or biased data can lead to inaccurate predictions.

Lack of Hardware Resources: We started training our model on Google colab but it failed as it had time limit of 2 hours only on free credit. So, we shifted to Kaggle Notebook and trained our model there.

Training of the model: Training a model is a very time consuming process and require high computing power it took majority of our time just for the model to be trained. It took more than 4 hours.