Define a function to forecast next value in python LSTM Model AI/ML

I have a good hands on working with Advanced R and Python and BI tools and technologies, AI, Big Data. I have quite a good knowledge of DL/ML Algorithm , have also developed Dashboards and Web Applications using flask/django. My area of expertise is building financial models (Stock Markets) , Image Processing and building models for food, healthcare and telecom sector, Classification/Prediction/Clustering, NLP and Chatbots. Specifically, I have worked for long on machine learning and Deep learning including computer Vision and natural language processing and model building for the same. I understand the project requirement and will deliver the desired product within the time specified. I would like to hear from you. Thanks Shivam

CONSIDER YOUR JOB DONE I have reviewed your python script & will complete it. Some of my past work is: COURSE SUGGESTING CHATBOT |RASA NLU,SPACY,BOTKIN, NLTK, PANDAS This project is being developed as a course suggesting assistant for the training and education wing of CETPA. It asks for certain questions regarding educational background, interests, time etc. and suggest certain choices based on the knowledge acquired in an interactive manner. CHURN PREDICTION ON TELECOM DATASET | SCIKIT-LEARN, FLASK, PANDAS, NUMPY, AUTOMOBILE DATA ANALYTICS | R,GGPLOT2, SHINY, RSTUDIO The dataset was analyzed using EDA and several potential relationships between automobile characteristics were identified and a predictive model to classify pricing into two categories was created using Regression. MATPLOTLIB Telecom dataset used to predict churning was first wrangled using Pandas and then different scikit-learn models were applied on the refined dataset .K-fold cross validation was used to suggest the best performing model. Confusion matrix with and without normalization was also generated. Web-application using Flask is used to deploy the model in real-time. PREDICTING LOAN DEFAULT | SCIKIT-LEARN, PANDAS, matplotlib Built an ensemble of XGBoost and Gradient Boosting model to predict the customer default in a Bank. Cleaned and analyzed the data and engineered new features using the insights obtained by analyzing the data. Achieved a decent accuracy of 72% on the reasonable-sized data

Hi there, I have advanced knowledge in python inluding keras machine learning library and I can help you. Just contact me, so we can start to discuss your problem. Best