b.    TOTAL SUM    =10032628.9 AVERAGE         =3553.88907 STD DEV          =1841.86511 VARIANCE        =3392467.07 MAXIMUM        =14082.8 MINIMUM         =482.13

GOOGLE DRIVE LINK FOR MY PROJECT: https://drive.google.com/drive/folders/1QfhpIB5rGihWTncYoqvD449dSuBjfvpu?usp=share_link Data summary of all male clients: DSUM  = 20318041019 DAVERAGE= 193396.4822 Data Summary for all Flag Columns: FLAG 2=DCOUNT()=307511 FLAG 3=DCOUNT()=307511 Other Deliveralbes: Macro and Freezing…

The project link is given below: https://public.tableau.com/views/PROJECT1JOELP/Dashboard1?:language=en-US&:display_count=n&:origin=viz_share_link

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Analyzing a user's database involves examining and understanding the information stored in the user's collection of data. This process helps in identifying patterns, relationships, and insights within the data. The goal is to gain a clear understanding of what the data contains, how it is organized, and what valuable information can be extracted from it.  

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EDA on Vehicle Insurance Customer Data:                              The Project is about the Customer Data and Customer Policy Data.In this Customer data  I used several functions to clear the data and remove the null values and replaced the null values into mean after that some case correction and removed the duplicates…

In the English Dictionary i just used the serialisation text and i created the dictionary and add the word and its meaning.In library Book Management I just used the tkinter and for the backend i used the Sql database that was really interesting to do that frame.In the frame they asked to display the records which match the search criteria.

1.The data is medical insurance charges, with each row representing a single patient and various features about that patient. The features included in this dataset are: This is a supervised learning problem, as the data includes both input features and a target output (the charges). The goal of a supervised learning model…

No null values Both wines data is checked note: a NULL means that there is no value, we're looking at a blank/empty cell, and 0 means the value itself is 0. So 0 is not considered as a null value After removing all duplicates in each column in both wine data inly a few rows are survived for red wine, combined data…

Logistic Regression Accuracy : 0.7951219512195122 Decision Tree Accuracy : 0.9853658536585366 Random Forest Accuracy : 0.9853658536585366 SVM Accuracy : 0.8048780487804879 KNN Accuracy : 0.9853658536585366These are the Five model algorithm Decision Tree, Random Forest, and KNN models are performing exceptionally well based…

By following this structured approach, you can effectively classify countries based on critical development indicators and make informed decisions on where to allocate funds for maximum impact.

Population vs. Sample Variance:Population: Divide by NNN.Sample: Divide by n−1n-1n−1 to reduce bias (Bessel's correction).Stratified vs. Clustered Sampling:Stratified: Divide population into groups, then sample from each group.Cluster: Divide population into groups, then sample whole groups.Number of Samples from Population…

1. Calculate All 4 Business MomentsMean: Average of data.Variance: Spread of data around the mean.Skewness: Symmetry of data (positive or negative).Kurtosis: "Peakedness" or tail heaviness of data.2. Expected Value vs. MeanExpected value is used in probability distributions for long-term averages, while the mean is a simple…

1. Perform Gradient Descent in Python (Example Code)# Example: Minimizing Mean Squared Error (MSE) Lossimport numpy as npX = np.array([1, 2, 3])y = np.array([2, 4, 6])theta = 0learning_rate = 0.01iterations = 100for _ in range(iterations):    gradient = -(2 / len(y)) * np.sum((y - (theta * X)) * X)   …

1. What Is a Neural Network?A neural network is a computer model inspired by the human brain, consisting of layers of nodes (neurons) that work together to learn patterns from data.2. What Is Deep Learning?Deep learning is a type of machine learning that uses neural networks with many layers (deep networks) to learn complex…

In summary, data cleaning for the Automobile 1985 dataset involves handling missing values, converting categorical variables, removing duplicates, and addressing outliers. Descriptive analytics helps summarize the data using statistical measures, visualizations, and correlations, providing valuable insights into the dataset's…

Load the Car Dataset.Preprocessed the Data by handling missing values and scaling.Applied K-Means Clustering to group the cars based on their features.Predicted Classes based on cluster labels.I used this approach so you can easily identify the types of cars and their respective features, aiding in decision-making processes…

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