As answer is uploaded as file  python_project_1_english_dictionary_app.py

I uploaded my answer as exe

1. Why there is a difference in the formula of variance for population and sample For Both populations, we first need to find the difference of mean and Standard deviation then square all the deviations and sum all the squared deviation values  Here comes the point where for population variance we divide by the number…

1)Calculate all 4 business moments using pen and paper for the below data set? Mean = 1.4 Variance =1.84 Skewness=0.572( moderate) Kurtosis=-0.9522(platykurtic)   2)What is the significance of expected value when simple mean (Sum of all observations/number of observations) is already in place Expected value is equal…

1.Perform Gradient Descent in Python with any loss function def gradient_descent(x,y,iteration=30): m_current=b_current=0 #intialize m and b learning_rate =0.0004 # step's n=len(x) for i in range(iteration): y_predict = (m_current*x)+b_current #y=mx+b cost = (1/n) * sum( [val**2 for val in ( y-y_predict )]) # mean_squared_error…

 1. Pros and cons of SVM Advantages: effective in a higher dimension, margin separation, memory effective  Disadvantages: size of the dataset, the noise of data, no probabilistic explanation    2. Apply SVM to the “Surface defects in stainless steel plates” dataset and evaluate it import…

1. Apply knn to the “Surface defects in stainless steel plates” and identify the differences pipe_df=pd.read_csv('faults.csv')X=pipe_df.iloc[:,:-1]y=pipe_df['Other_Faults'] # Splitting the dataset into training and test set. from sklearn.model_selection import train_test_split x_train, x_test, y_train, y_test=…

1. What is a Neural Network? It is the algorithm that recognizes the pattern of data like mimics the human brain (neuron)   2. What is deep learning? It is computer learning to think using a structural model, It is a type of Machine learning where Machine Learning is a subset of Artificial Intelligence and  neural…

1. How does similarity is calculated if data is categorical in nature We have Kmean to cluster data but it uses the distance between data to find clusters So we need Kmode  Kmode clustering is one of the unsupervised Machine learning techniques used to cluster categorical variables, It uses dissimilarity(total difference)…

  https://www.kaggle.com/plutosenthil/analysis1   https://www.kaggle.com/plutosenthil/analysis1

https://www.kaggle.com/plutosenthil/credit-card-dataset-for-clustering   https://www.kaggle.com/plutosenthil/credit-card-dataset-for-clustering

https://www.kaggle.com/plutosenthil/microsoft-stock-data   https://www.kaggle.com/plutosenthil/microsoft-stock-data

I can't able to find csv So I tried to solve similar problem   https://www.kaggle.com/plutosenthil/backorder-prediction   https://www.kaggle.com/plutosenthil/backorder-prediction  

Answer as file

Answer as file

Answer as a file

Answer as file    I have used  school-performance .csv   CREATE DATABASE alumini CREATE TABLE if not exists alumini.performance (DBN varchar(20),Quality_Review_Year year,Quality_Review_Score varchar(255),Progress_Rpt_10_11 char(10),Student_Progress_10_11 char(10),Student_Perf_10_11 char(10),Envi_10_11…

https://www.kaggle.com/plutosenthil/eda-logistic-regression   https://www.kaggle.com/plutosenthil/eda-logistic-regression

https://www.kaggle.com/plutosenthil/medical-cost-personal-datasets-neural   https://www.kaggle.com/plutosenthil/medical-cost-personal-datasets-neural

https://www.kaggle.com/plutosenthil/descent-work   https://www.kaggle.com/plutosenthil/descent-work

Answer as a file

 Answer as file

Answer as a file

    Advantages of Gradient Boosting provides predictive accuracy, flexibility, No data pre-processing required, Handles missing data Disadvantages of Gradient Boosting overfitting, expensive

https://www.kaggle.com/plutosenthil/machine-failure-data   https://www.kaggle.com/plutosenthil/machine-failure-data

Answer as file

 https://www.kaggle.com/plutosenthil/vehicle-recognition   https://www.kaggle.com/plutosenthil/vehicle-recognition

Gradient descent algorithm finds the global minimum of the cost function In which information travels through the neural network from input neurons to the output neurons, while the error is calculated and propagated back through the network to update the weights. In RNNs, information from previous time points is used as…

https://www.kaggle.com/plutosenthil/sms-spam   https://www.kaggle.com/plutosenthil/sms-spam

https://www.kaggle.com/plutosenthil/credit-card-fraud-detection   https://www.kaggle.com/plutosenthil/credit-card-fraud-detection