Trending February 2024 # Boldly Bring Them Back: Interventions For Student Reengagement And Dropout Prevention # Suggested March 2024 # Top 10 Popular

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Schools can work to inspire confidence in students who have fallen behind by offering consistent support, starting in the summer.

School administrators across the nation have seen a lack of student engagement during Covid-19. The school structure during the pandemic has made it easier for students to disengage from school, leading to random pop-ins on Google Meets and sporadic responses to emails. This lack of engagement and focus places students at risk of dropping out of high school. 

In order to prevent this, school administrators can seek out opportunities to intervene and reengage those students. Fundamentally, students want and need to feel connected and cared for. When schools uphold their responsibility to provide social and emotional support, the consistent and intentional connection with students can support engagement and prevent dropouts. Summer and early fall is a good time to implement practical interventions to help disengaged students before the issue progresses.

Mobile Mondays 

Visiting students’ homes during the summer is a meaningful tool for reengagement. This can be done as often as a district likes, with a specifically designated team of administrators, teachers, and support staff.

Try visiting homes every Monday during the summer. This should not be done in the style of a typical truancy home visit—show care and interest in the student’s well-being to identify and reduce barriers to their engagement. When done with care and intention, the visit shows that the school is committed to the student and welcomes them back.

This is a great opportunity to get to know the student better. Call ahead and schedule the home visit according to the student’s and family’s availability. It’s perfectly acceptable to visit in the front yard and socially distance during the pandemic.

R&R: Reengagement at Registration

Registration for the new school year is also a great time for reengagement. Use previously collected data to create a list of students who were disengaged during the previous semester or academic year. When the student arrives at the registration event, follow up with them. If your district or school does not have a formal registration process, invite families to come to the school before the school year begins. The location of where the following steps occur is flexible and can be adjusted to fit your school’s needs.

1. Hold a conference. Choose a private space for an administrator, teacher, or support staff to hold the conference. This provides an opportunity to create a relationship with the student and their parents.

2. Collect information. Provide a brief form asking questions to gather information about the reason for the student’s disengagement and identify their needs. Providing a list of possible options (e.g., family issues, mental health, employment) can be helpful. Schools can choose to address those needs as a preventive measure.

3. Provide a list of resources. The reengagement process includes sharing information with students about services that the school offers for support and school credit recovery. Social and emotional services are essential for this population of students. The list can include local agencies that partner with the district.

4. Create a check-in system. Every student benefits from having at least one supportive adult at their school. They can go to that adult throughout the school year for assistance as needed. That adult (or the school) can send ongoing motivational and encouraging emails or texts to the student. If a student can’t think of anyone on their own, the school can identify a teacher or support staff member who is available to help. The hope is that the adult and student will develop a strong rapport as they spend time checking in together.

5. Determine the student’s level of confidence. Students who lack self-efficacy need support to build their confidence and reduce self-inflicted barriers. There are plenty of self-efficacy measures available online, but simply asking students to rate their own level of confidence about whether they think that they can pass all their classes is acceptable (e.g., on a Likert scale: very confident, somewhat confident, not confident). 

Transition Track 

Accepting that some students won’t have the capacity to attend full days of school for five days per week is a realistic perspective. It may sound counterintuitive to reduce the instructional time for students who already missed so much, but some students might need to slowly transition back into the full-time school structure.

Students are unlikely to just bounce back from being disengaged for a year or two. They might feel overwhelmed by pressure to catch up with their peers. Discovering what works for the nontraditional student is key. A half-day program or another form of an abbreviated schedule might be more appropriate. Check with your state board of education in regard to seat-time and attendance policies. Some state boards issue waivers that allow students to use competency-based education in place of seat time.

If reducing the school day isn’t an option, try starting the school year earlier. Students who were disengaged can transition back to school a week or two before the rest of the student body. The transition time could focus on executing functioning skills and social and emotional learning. A program focused on these areas helps reduce some of the anxiety connected to attending school and failing behind. 

Your school can create a path for disengaged students to succeed.

You're reading Boldly Bring Them Back: Interventions For Student Reengagement And Dropout Prevention

Dos And Don’Ts Of Scar Prevention

As the warmer months approach, people who just had surgery may be thinking about getting their scars fixed. Most people know that any skin injury that is more serious than a cut or scrape will leave a scar, no matter how well it is treated.

How Do Scars Form?

How a scar looks depends on several things. In addition to the amount of blood that may get to the area and the color and thickness of your skin, the shape, size, and depth of the wound also affect how the scar looks.

Types of Scars

There are three different kinds of scars. Most scars are small, flat, and very thin. When someone gets a hypertrophic injury, the scars are often red, raised, and thick. Keloid scars are raised like hypertrophic scars, but they go past the edges of the original cut and are usually darker or redder.

Keloid scars are known for being hard to treat and hard to predict, and they often run in families. They happen when the body makes too much collagen and responds well to steroid injections in the area. You should see a doctor if you have a keloid scar.

There are some rules to follow if you are self-conscious about how your scars look. Now, let us look at the do’s and don’ts in detail below −

DO’s Do: Use Appropriate Dressings to Aid in Healing

People often think that wounds need to be left open so that they can “breathe.” When a wound is exposed to air, it dries out. It makes it heal in a longer time. When scabs form on dry wounds, they stop new skin from growing. Infections can make scars worse, so use antibiotic ointment and a bandage to keep them from happening. To make sure the wound heals completely, change the dressing and apply ointment on a regular schedule.

Do: Use A Scar Treating Method

You may already know that cocoa butter can help scars fade, but there are also many over-the-counter options. For example, scar removal gels can be used repeatedly without hurting either new or old scars. They mostly succeed in making the scar less noticeable by removing the color caused by the injury.

Do: Press Down

By getting more blood to the area and massaging the scars regularly, you can make the scars less noticeable and break down the collagen that holds them to the damaged tissue.

Do: Get Stitches if Necessary

For deep or widely spaced cuts to heal properly, a doctor may need to stitch them together. Don’t forget that the wound is at its weakest right after it has been cut so that stitches can be put in. A doctor may not want to close a wound that hasn’t been taken care of for too long because it could get infected. When the cut starts to heal, it might be harder to sew it back together. If you aren’t sure if your cut needs sutures, you should see a doctor as soon as possible.

Do: Be Patient

Healing takes time, and it could take quite a while. There is an initial healing period of three months and then a maintenance period of three months. The scar is almost finished at the one-year mark after an accident, but it will continue to grow and change over the next year.

DONT’S Don’t: Use Peroxide to Fresh Cuts

Peroxide is used to treat cuts in many families because that’s what our parents did when we were young. Even though peroxide worked well against infections, it also killed new skin cells, which made the scars more obvious.

Don’t: Expose Under Sunlight

Even if the weather is getting warmer, a wound that is still healing should be kept covered. Sun damage can cause the skin to change color, which can make a scar look bigger. Use sunscreen whenever you go outside to keep your scars from getting darker.

Don’t: Use Vitamin E

Word of mouth says that putting the liquid from a Vitamin E pill on the scar will help it heal faster. Not only does scientific research show that Vitamin E doesn’t do much good, but many people who try it have a bad reaction to it.

Don’t: Believe False Scar Creams Advertisements

There have been anecdotal reports that vitamin E helps reduce scars, but this has never been proven in a rigorous scientific study. In reality, no scar-prevention product has been shown in clinical trials to work much better than other options that don’t require a prescription.

Don’t: Pick At Scabs

Patients may pick at scabs because they are itchy, but this can slow the healing of the wound and make it more likely that the scar will be too dark.

When a wound happens in the body, the healing process starts right away. White blood cells kill germs that cause infections, and red blood cells, fibrin, and platelets work together to form a clot to seal the wound and make a scab.

If the scab is taken off too soon after the wound has healed, it can worsen the scar.

Conclusion

Inflammation, tissue creation, and remodeling are the three stages of the natural scar formation process that occur as part of the skin’s normal healing process. The proliferation of fibroblasts in the wound causes collagen production, which is then used to fill and chaotically seal the wound since most patients aim to have as little visible scarring as possible after surgery, especially if the scar is in a highly visible area. Hence, we hope our Dos and Don’ts of Scar Prevention article has helped you!

Scars take a long time to disappear gradually. To prevent the scar tissue from drying out and changing appearance due to exposure to UV radiation, use a mix of basic Vaseline with sunscreen or silicone sheets. Topically Vitamin E is ineffective and may lead to localized contact dermatitis. See a dermatologist if you’re interested in exploring more cutting-edge treatment options.

Student Performance Analysis And Prediction

Introduction

Read more on what is predictive analytics for beginners here.

This article was published as a part of the Data Science Blogathon.

Table of Contents Understanding the Problem Statement

This project understands how the student’s performance (test scores) is affected by other variables such as Gender, Ethnicity, Parental level of education, and Lunch and Test preparation course.

The primary objective of higher education institutions is to impart quality education to their students. To achieve the highest level of quality in the education system, knowledge must be discovered to predict student enrollment in specific courses, identify issues with traditional classroom teaching models, detect unfair means used in online examinations, detect abnormal values in student result sheets, and predict student performance. This knowledge is hidden within educational datasets and can be extracted through data mining techniques.

Data Collection

Dataset Source – Students performance chúng tôi The data consists of 8 column and 1000 rows.

Import Data and Required Packages

Importing Pandas, Numpy, Matplotlib, Seaborn and Warings Library.

import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns import warnings warnings.filterwarnings("ignore")

Import the CSV Data as Pandas DataFrame

df = pd.read_csv("data/StudentsPerformance.csv")

Show the top 5 Recoreds

df.head()

show the top 5 records on the dataset and look at the features.

To see the shape of the dataset

df.shape

And it will help to find the shape of the dataset.

Dataset Information

lunch: having lunch before test (standard or free/reduced)

test preparation course: complete or not complete before test

math score

reading score

writing score

After that, we check the data as the next step. There are a number of categorical features contained in the dataset, including multiple missing value kinds, duplicate values, check data types, and a number of unique value types.

Data Checks to Perform

Check Missing values

Check Duplicates

Check data type

Check the number of unique values in each column

Check the statistics of the data set

Check various categories present in the different categorical column

Check Missing Values

To check every column of the missing values or null values in the dataset.

df.isnull().sum()

If there are no missing values in the dataset.

Check Duplicates

If checking the our dataset has any duplicated values present or not

df.duplicated().sum()

There are no duplicates values in the dataset.

Check the Data Types

To check the information of the dataset like datatypes, any null values present in the dataset.

#check the null and Dtypes df.info() Check the Number of Unique Values in Each Column df.nunique() Check Statistics of the Data Set

To examine the dataset’s statistics and determine the data’s statistics.

Insight

The numerical data shown above shows that all means are fairly similar to one another, falling between 66 and 68.05.

The range of all standard deviations, between 14.6 and 15.19, is also narrow.

While there is a minimum score of 0 for math, the minimums for writing and reading are substantially higher at 10 and 17, respectively.

We don’t have any duplicate or missing values, and the following codes will provide a good data checking.

Exploring Data print("Categories in 'gender' variable: ",end=" ") print(df["gender"].unique()) print("Categories in 'race/ethnicity' variable: ",end=" ") print(df["race/ethnicity"].unique()) print("Categories in 'parental level of education' variable: ",end=" ") print(df["parental level of education"].unique()) print("Categories in 'lunch' variable: ",end=" ") print(df["lunch"].unique()) print("Categories in 'test preparation course' variable: ",end=" ") print(df["test preparation course"].unique())

The unique values in the dataset will be provided and presented in a pleasant way in the code above.

The output will following:

We define the numerical and categorical columns:

#define numerical and categorical columns numeric_features = [feature for feature in df.columns if df[feature].dtype != "object"] categorical_features = [feature for feature in df.columns if df[feature].dtype == "object"] print("We have {} numerical features: {}".format(len(numeric_features),numeric_features)) print("We have {} categorical features: {}".format(len(categorical_features),categorical_features))

The above code will use separate the numerical and categorical features and count the feature values.

Exploring Data (Visualization) Visualize Average Score Distribution to Make Some Conclusion

Histogram

Kernel Distribution Function (KDE)

Histogram & KDE

Gender Column

How is distribution of Gender?

Is gender has any impact on student’s performance?

# Create a figure with two subplots f,ax=plt.subplots(1,2,figsize=(8,6)) # Create a countplot of the 'gender' column and add labels to the bars sns.countplot(x=df['gender'],data=df,palette ='bright',ax=ax[0],saturation=0.95) for container in ax[0].containers: ax[0].bar_label(container,color='black',size=15) # Set font size of x-axis and y-axis labels and tick labels ax[0].set_xlabel('Gender', fontsize=14) ax[0].set_ylabel('Count', fontsize=14) ax[0].tick_params(labelsize=14) # Create a pie chart of the 'gender' column and add labels to the slices plt.pie(x=df['gender'].value_counts(),labels=['Male','Female'],explode=[0,0.1],autopct='%1.1f%%',shadow=True,colors=['#ff4d4d','#ff8000'], textprops={'fontsize': 14}) # Display the plot plt.show()

Gender has balanced data with female students are 518 (48%) and male students are 482 (52%)

Race/Ethnicity Column # Define a color palette for the countplot colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd'] # blue, orange, green, red, purple are respectiively the color names for the color codes used above # Create a figure with two subplots f, ax = plt.subplots(1, 2, figsize=(12, 6)) # Create a countplot of the 'race/ethnicity' column and add labels to the bars sns.countplot(x=df['race/ethnicity'], data=df, palette=colors, ax=ax[0], saturation=0.95) for container in ax[0].containers: ax[0].bar_label(container, color='black', size=14) # Set font size of x-axis and y-axis labels and tick labels ax[0].set_xlabel('Race/Ethnicity', fontsize=14) ax[0].set_ylabel('Count', fontsize=14) ax[0].tick_params(labelsize=14) # Create a dictionary that maps category names to colors in the color palette color_dict = dict(zip(df['race/ethnicity'].unique(), colors)) # Map the colors to the pie chart slices pie_colors = [color_dict[race] for race in df['race/ethnicity'].value_counts().index] # Create a pie chart of the 'race/ethnicity' column and add labels to the slices plt.pie(x=df['race/ethnicity'].value_counts(), labels=df['race/ethnicity'].value_counts().index, explode=[0.1, 0, 0, 0, 0], autopct='%1.1f%%', shadow=True, colors=pie_colors, textprops={'fontsize': 14}) # Set the aspect ratio of the pie chart to 'equal' to make it a circle plt.axis('equal') # Display the plot plt.show()

Most of the student belonging from group C /group D.

Lowest number of students belong to group A.

Parental Level of Education Column plt.rcParams['figure.figsize'] = (15, 9) plt.style.use('fivethirtyeight') sns.histplot(df["parental level of education"], palette = 'Blues') plt.title('Comparison of Parental Education', fontweight = 30, fontsize = 20) plt.xlabel('Degree') plt.ylabel('count') plt.show()

Largest number of parents are from college.

Bivariate Analysis df.groupby('parental level of education').agg('mean').plot(kind='barh',figsize=(10,10)) plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.) plt.show()

The score of student whose parents possess master and bachelor level education are higher than others.

Maximum Score of Students in All Three Subjects plt.fig figsize=(18,8)) plt.subplot(1, 4, 1) plt.title('MATH SCORES') sns.violinplot(y='math score',data=df,color='red',linewidth=3) plt.subplot(1, 4, 2) plt.title('READING SCORES') sns.vi plot(y='reading score',data=df,color='green',linewidth=3) plt.subplot(1, 4, 3) plt.title('WRITING SCORES') sns.violinplot(y='writing score',data=df,color='blue',linewidth=3) plt.show()

From the above three plots its clearly visible that most of the students score in between 60-80 in Maths whereas in reading and writing most of them score from 50-80.

Multivariate Analysis Using Pie Plot # Set figure size plt.rcParams['figure.figsize'] = (12, 9) # First row of pie charts plt.subplot(2, 3, 1) size = df['gender'].value_counts() labels = 'Female', 'Male' color = ['red','green'] plt.pie(size, colors=color, labels=labels, autopct='%.2f%%') plt.title('Gender', fontsize=20) plt.axis('off') plt.subplot(2, 3, 2) size = df['race/ethnicity'].value_counts() labels = 'Group C', 'Group D', 'Group B', 'Group E', 'Group A' color = ['red', 'green', 'blue', 'cyan', 'orange'] plt.pie(size, colors=color, labels=labels, autopct='%.2f%%') plt.title('Race/Ethnicity', fontsize=20) plt.axis('off') plt.subplot(2, 3, 3) size = df['lunch'].value_counts() labels = 'Standard', 'Free' color = ['red', 'green'] plt.pie(size, colors=color, labels=labels, autopct='%.2f%%') plt.title('Lunch', fontsize=20) plt.axis('off') # Second row of pie charts plt.subplot(2, 3, 4) size = df['test preparation course'].value_counts() labels = 'None', 'Completed' color = ['red', 'green'] plt.pie(size, colors=color, labels=labels, autopct='%.2f%%') plt.title('Test Course', fontsize=20) plt.axis('off') plt.subplot(2, 3, 5) size = df['parental level of education'].value_counts() labels = 'Some College', "Associate's Degree", 'High School', 'Some High School', "Bachelor's Degree", "Master's Degree" color = ['red', 'green', 'blue', 'cyan', 'orange', 'grey'] plt.pie(size, colors=color, labels=labels, autopct='%.2f%%') plt.title('Parental Education', fontsize=20) plt.axi ff') # Remove extra subplot plt.subplot(2, 3, 6).remove() # Add super title plt.suptitle('Comparison of Student Attributes', fontsize=20, fontweight='bold') # Adjust layout and show plot # This is removed as there are only 5 subplots in this figure and we want to arrange them in a 2x3 grid. # Since there is no 6th subplot, it is removed to avoid an empty subplot being shown in the figure. plt.tight_layout() plt.subplots_adjust(top=0.85) plt.show()

The number of Male and Female students is almost equal.

The number of students is higher in Group C.

The number of students who have standard lunch is greater.

The number of students who have not enrolled in any test preparation course is greater.

The number of students whose parental education is “Some College” is greater followed closely by “Associate’s Degree”.

From the above plot, it is clear that all the scores increase linearly with each other.

Student’s Performance is related to lunch, race, and parental level education.

Females lead in pass percentage and also are top-scorers.

Student Performance is not much related to test preparation course.

The finishing preparation course is beneficial.

Model Training

Import Data and Required Packages

Importing scikit library algorithms to import regression algorithms.

# Modelling from sklearn.metrics import mean_squared_error, r2_score from sklearn.neighbors import KNeighborsRegressor from chúng tôi import DecisionTreeRegressor from sklearn.ensemble import RandomForestRegressor,AdaBoostRegressor from chúng tôi import SVR from sklearn.linear_model import LinearRegression,Lasso from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error from sklearn.model_selection imp RandomizedSearchCV from catboost import CatBoostRegressor from xgboost import XGBRegressor import warnings Splitting the X and Y Variables

This separation of the dependent variable(y) and independent variables(X) is one the most important in our project we use the math score as a dependent variable. Because so many students lack in math subjects it will almost 60% to 70% of students in classes 7-10 students are fear of math subjects that’s why I am choosing the math score as a dependent score.

It will use to improve the percentage of math scores and increase the grad f students and also remove fear in math.

X = df.drop(columns="math score",axis=1) y = df["math score"] Create Column Transformer with 3 Types  of Transformers num_features = X.select_dtypes(exclude="object").columns cat_features = X.select_dtypes(include="object").columns from sklearn.preprocessing import OneHotEncoder,StandardScaler numeric_transformer = StandardScaler() oh_transformer = OneHotEncoder() preprocessor = Column transformer( [ ("OneHotEncoder", oh_transformer, cat_features), ("StandardScaler", numeric_transformer, num_features), ] ) X = preprocessor.fit_transform(X) Separate Dataset into Train and Test

To separate the dataset into train and test to identify the training size and testing size of the dataset.

from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2,random_state=42) X_train.shape, X_test.shape

Create an Evaluate Function for Model Training

This function is use to evaluate the model and build a good model.

def evaluate_model(true, predicted): mae = mean_absolute_error(true, predicted) mse = mean_squared_error(true, predicted) rmse = np.sqrt(mean_squared_error(true, predicted)) r2_square = r2_score(true, predicted) return mae, mse, rmse, r2_square

To create a models variable and form a dictionary formate.

models = { "Linear Regression": LinearRegression(), "Lasso": Lasso(), "K-Neighbors Regressor": KNeighborsRegressor(), "Decision Tree": DecisionTreeRegressor(), "Random Forest Regressor": RandomForestRegressor(), "Gradient Boosting": GradientBoostingRegressor(), "XGBRegressor": XGBRegressor(), "CatBoosting Regressor": CatBoostRegressor(verbose=False), "AdaBoost Regressor": AdaBoostRegressor() } model_list = [] r2_list =[] for i in range(len(list(models))): model = list(models.values())[i] model.fit(X_train, y_train) # Train model # Make predictions y_train_pred = model.predict(X_train) y_test_pred = model.predict(X_test) # Evaluate Train and Test dataset model_train_mae, model_train_mse, model_train_rmse, model_train_r2 = evaluate_model(y_train, y_train_pred) model_test_mae, model_test_mse, model_test_rmse, model_test_r2 = evaluate_model(y_test, y_test_pred) print(list(models.keys())[i]) model_list.append(list(models.keys())[i]) print('Model performance for Training set') print("- Root Mean Squared Error: {:.4f}".format(model_train_rmse)) print("- Mean Squared Error: {:.4f}".format(model_train_mse)) print("- Mean Absolute Error: {:.4f}".format(model_train_mae)) print("- R2 Score: {:.4f}".format(model_train_r2)) print('Model performance for Test set') print("- Root Mean Squared Error: {:.4f}".format(model_test_rmse)) print("- Mean Squared Error: {:.4f}".format(model_test_rmse)) print("- Mean Absolute Error: {:.4f}".format(model_test_mae)) print("- R2 Score: {:.4f}". for model_test_r2)) r2_list.append(model_test_r2) print('='*35) print('n')

The output of before tuning all algorithms’ hyperparameters. And it provides the RMSE, MSE, MAE, and R2 score values for training and test data.

Hyperparameter Tuning

It will give the model with most accurate predictions and improve prediction accuracy.

This will give the optimized value of hyperparameters, which maximize your model predictive accuracy.

from sklearn.model_selection import GridSearchCV, RandomizedSearchCV from sklearn.metrics import make_scorer # Define hyperparameter ranges for each model param_grid = { "Linear Regression": {}, "Lasso": {"alpha": [1]}, "K-Neighbors Regressor": {"n_neighbors": [3, 5, 7],}, "Decision Tree": {"max_depth": [3, 5, 7],'criterion':['squared_error', 'friedman_mse', 'absolute_error', 'poisson']}, "Random Forest Regressor": {'n_estimators': [8,16,32,64,128,256], "max_depth": [3, 5, 7]}, "Gradient Boosting": {'learning_rate':[.1,.01,.05,.001],'subsample':[0.6,0.7,0.75,0.8,0.85,0.9], 'n_estimators': [8,16,32,64,128,256]}, "XGBRegressor": {'depth': [6,8,10],'learning_rate': [0.01, 0.05, 0.1],'iterations': [30, 50, 100]}, "CatBoosting Regressor": {"iterations": [100, 500], "depth": [3, 5, 7]}, "AdaBoost Regressor": {'learning_rate':[.1,.01,0.5,.001],'n_estimators': [8,16,32,64,128,256]} } model_list = [] r2_list =[] for model_name, model in models.items(): # Create a scorer object to use in grid search scorer = make_scorer(r2_score) # Perform grid search to find the best hyperparameters grid_search = GridSearchCV( model, param_grid[model_name], scoring=scorer, cv=5, n_jobs=-1 ) # Train the model with the best hyperparameters model_test_r2)) r2_list.append(model_test_r2) print('='*35) print('n') Outputs

The output of after tuning all algorithms’ hyperparameters. And it provides the RMSE, MSE, MAE, and R2 score values for training and test data.

If we choose Linear regression as the final model because that model will get a training set r2 score is 87.42 and a testing set r2 score is 88.03.

Model Selection

This is used to select the best model of all of the regression algorithms.

In linear regression, we got 88.03 curacy in all of the regression models that’s why we choose model.

pd.DataFrame(list(zip(model_list, r2_list)), columns=['Model Name', 'R2_Score']).sort_values(by=["R2_Score"],ascending=False)

 Accuracy of the model is 88.03%

plt.scatter(y_test,y_pred) plt.xlabel('Actual') plt.ylabel('Predicted') plt.show()

sns.regplot(x=y_test,y=y_pred,ci=None,color ='red')

Difference Between Actual and Predicted Values pred_df=pd.DataFrame({'Actual Value':y_test,'Predicted Value':y_pred,'Difference':y_test-y_pred}) pred_df

Convert the Model to Pickle File # loading library import pickle # create an iterator object with write permission - model.pkl with open('model_pkl', 'wb') as files: pickle.dump(model, files) # load saved model with open('model_pkl' , 'rb') as f: lr = pickle.load(f) Conclusion

This brings us to an end to the student’s performance prediction. Let us review our work. First, we started by defining our problem statement, looking into the algorithms we were going to use and the regression implementation pipeline. Then we moved on to practically implementing the identification and regression algorithms like Linear Regression, Lasso, K-Neighbors Regressor, Decision Tree, Random Forest Regressor, XGBRegressor, CatBoosting Regressor, and AdaBoost Regressor. Moving forward, we compared the performances of these models. Lastly, we built a Linear regression model that proved that it works best for student performance prediction problems.

The key takeaways from this  student performance prediction are:

Identification of student performance prediction is important for many institutions.

Linear regression gives better accuracy compared to other regression problems.

Linear regression is the best fit for the problem

Linear regression provides an accuracy of 88%, giving out the most accurate results.

I hope you like my article on “Student performance analysis and prediction.” The entire code can be found in my GitHub repository. You can connect with me here on LinkedIn.

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion. 

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How To Choose The Best Intrusion Prevention System For Your Needs

Cyber threats pose significant risks to organizations of all sizes, making robust security measures imperative. An intrusion prevention system (IPS) is one critical component in an organization’s cybersecurity arsenal, acting as a vigilant gatekeeper to actively monitor network traffic and prevent unauthorized access and malicious attacks. Choosing the right IPS can depend on everything from whether it is network-based or hosted to how well it integrates with existing systems and how much it costs.

We’ve rounded up the best intrusion prevention systems to help make the selection process less daunting. Here are our top picks:

Here’s a look at how the top IPSs compared based on key features.

Real-Time Alerts Integration with Other Security Systems Type of Intrusion Detection Automatic Updates Pricing

Cisco Secure Next-Generation Intrusion Prevention System Yes Yes Network-based Yes On-contact

Fidelis Network Yes Yes Network-based Yes 15-day free trial

Palo Alto Networks Threat Prevention Yes Yes Network-based and host-based Yes Free trial

Trellix Intrusion Prevention System Yes Yes Network-based and host-based Yes On-contact

Jump to:

Best for comprehensive network security

This highly flexible solution can be easily deployed across different network environments as its open architecture supports Amazon Web Services (AWS), VMWare, Azure, and other hypervisors.

Enhanced visibility with Firepower Management Center

Constantly updated early-warning system

Flexible deployment options for inline inspection or passive detection

Cisco Threat Intelligence Director for third-party data ingestion

Real-time data inputs optimize data security

Easy integration without major hardware changes

High scalability with purpose-built solutions

Expensive for small-scale organizations

Initial integration challenges

Cisco offers free trials for most products, including its IPS, but does not make its pricing readily available. For details, contact Sales Support.

Best for Advanced Threat Detection Response

This specific network defense solution helps prevent future breaches with both real-time and retrospective analysis.

Patented Deep Session Inspection for data exfiltration

Improved response with the MITRE ATT&CK framework and intelligence feed from Fidelis Cybersecurity

Unified network detection and response (NDR) solution for simplified network security

Customizable real-time content analysis rules for proactive network security

Faster threat analysis and improved security efficiency

Deeper visibility and threat detection with more than 300 metadata attributes

Single-view and consolidated network alerts with rich cyber terrain mapping

Complex configuration and setup

High-traffic environments cause network latency

Tighter integration with other tools is required

Fidelis Network offers a 15-day free trial, and will schedule a demo before it to show off the system’s capabilities and features.

Best for Zero-Day Exploits

ML-Powered NGFWs for complete visibility

Customized protection with Snort and Suricata signature support

Real-time analysis with enhanced DNS Security Cloud Service

Latest security updates from Advanced WildFire

Ultra low-latency native cloud service

Combined App-ID and User-ID identification technologies

Customized vulnerability signatures

Complete DNS threat coverage

Overly complex implementation for simple configurations

High upfront costs

Palo Alto Networks offers free trials, hands-on demos, and personalized tours for its products and solutions, but does not make its pricing models publicly available. Contact sales for details.

Best for On-Prem and Virtual Networks

Botnet intrusion detection across the network

Enhanced threat correlation with network threat behavior analysis

Inbound and outbound SSL decryption

East-west network visibility

Both signature-based and signature-less intrusion detection

Unified physical and virtual security

Maximum security and performance (scalability up to 100 Gbps)

Shared licensing and throughput model

Older variants and models still exist

Confusion pricing options

High rates of false positives

Schedule a demo to learn whether Trellix meets specific requirements. The vendor does not make pricing models publicly available; contact sales.

When deciding on an intrusion prevention system, make sure the features and capabilities match specific needs. Key features include the following:

Proactive threat detection and prompt incident response require real-time visibility. Timely alerts help implement preventive measures before any significant damage to the security posture. Advanced IPSs have real-time monitoring capabilities to identify potential vulnerabilities and minimize the impact of security incidents.

Intrusion prevention systems cannot operate in isolation. For the efficient protection of the entire business security infrastructure, they must integrate with other security solutions and platforms for a coordinated response. This also helps with the centralized management of security incidents.

There are mainly two types of intrusion detection: network-based and host-based. While network-based intrusion detection examines and analyzes the network traffic for vulnerabilities, host-based intrusion detection checks individual systems like servers, endpoints, or particular assets.

Automatic updates can help ensure an IPS adapt to the continuously evolving threat landscape of new threats and newly discovered vulnerabilities. They can also help keep pace with changing compliance and regulatory requirements and implement the latest security patches.

Threat intelligence helps an IPS enhance detection capabilities and minimize vulnerabilities with efficient mitigation strategies. With threat intelligence capabilities, IPS solutions access timely and actionable information to develop effective response strategies.

Here are some factors to consider when choosing an IPS:

There are broadly four types of IPS configurations depending on the network environment, security policies, and requirements where they will be implemented: network-based, host-based, wireless, and network behavior analysis system. Multiple configurations can also support complex pathways.

Intrusion prevention systems use different detection techniques to identify malicious activities—primarily signature-based, anomaly-based, and protocol-based. Signature-based detection helps detect consistent cyber threat patterns from a static list of known signatures, while anomaly-based detection can detect abnormalities within normal activity patterns. Protocol-based systems offer the flexibility to set references for benign protocol activities.

Intrusion prevention systems can be integrated using dedicated hardware and software, or incorporated within existing enterprise security controls. Businesses that don’t want to upgrade system architecture or invest in products or resources can rely on managed service providers for security, but an IPS purchased and installed on the network offers more control and authority.

Intrusion detection systems help detect security incidents and threats and send alerts to the Security Operations Center (SOC). Issues are investigated by security personnel and countermeasures executed accordingly. Essentially, they’re monitoring tools. While intrusion prevention systems also detect potential threats and malicious incidents, they automatically take appropriate actions, making them highly proactive, control-based cybersecurity solutions.

Intrusion prevention systems are key to enterprise security as they help prevent serious and sophisticated attacks. Some of the key benefits of IPS for businesses are:

Reduced strain on IT teams through automated response

Customized security controls as per requirements

Improved performance by filtering out malicious traffic

In order to provide an objective and comprehensive comparison of the various IPSs available in the market, we followed a structured research methodology. We defined evaluation criteria, conducted market research, collected data on each solution, evaluated and scored them, cross-verified our findings, and documented the results. Additionally, we considered user reviews and feedback to gain valuable insights into the real-world performance and customer satisfaction of each intrusion prevention solution.

The top intrusion prevention systems all work to protect enterprise networks from the ever-present, always evolving threat of cyberattack, but some stand out for different use cases. Selecting the right one will depend on the organization’s security needs, goals, and budget. Regular evaluation and updates are crucial to staying ahead of evolving threats and ensuring a robust security posture—the right IPS can enhance network security, protect sensitive data, and safeguard a business against potential cyber threats.

Sharp Back Pain: Stabbing Pain In Back

A sudden sharp pain in the back can stop anyone in their tracks and the cause of a sudden sharp back pain or acute back pain is not always obvious. Different muscles, bones and connective tissues meet in the back; thus, individuals may experience back pain for a wide range of reasons.

The majority of people experience sharp back pain in the lower back as compared to the upper back. There are many common and less-common causes of sharp back pain, and they may be both mechanical and medical.

Sharp Back Pain: Common Causes

Sharp back pain which is caused by a mechanical problem with the bones, disks, ligaments, or muscles of the back is one of the most common types of back pain. Some of the most specific causes for acute or sharp back pain may include the following −

Muscle Spasm

It refers to prolonged contraction or stiffening of the back muscles, which are a result of trauma or repetitive strain. A muscle spasm can produce sharp back pain in either the upper or lower back.

A muscle strain or spasm might occur due to a simple action like suddenly bending down to pick something up or twisting while holding a heavy object. A sharp back pain due to muscle strain may cause a burning or tingling sensation or a radiating ache, especially in the lower back.

Slipped Disk

Slipped disk is also called a bulging disk, herniated disk, ruptured disk or pinched nerve. It occurs from the improper lifting of heavy objects or overly strenuous activity. One of the most common symptoms of a slipped disk is sciatica which occurs when sharp back pain shoots down through the buttocks into the legs.

Pain from sciatica may build up over time or come severely at once. It may vary from a dull ache or severe tearing to a burning feeling. The pain may be warm or sharp and may radiate from one side of the lower back down to the hip or buttocks.

Compression Fracture

It is a fracture of the vertebrae which are commonly known as spine bones. This fracture can be a result of trauma or osteoporosis (weakened bones) where the pain is often very sharp.

Osteomyelitis

When the vertebrae themselves are infected, this condition is called osteomyelitis. It is a very rare vertebrae infection where back pain is generally accompanied by fever and other symptoms.

Other Causes of Sharp Back Pain

The rupture of the main artery in the abdomen which is called a ruptured splenic artery aneurysm may cause sharp back pain. Moreover, pyelonephritis, which is a kidney infection and pleurisy, which is an infection of the lining of the lungs and chest, may mimic back pain.

Additionally, there might be other symptoms that may call for immediate medical attention such as back pain with fever, numbness or tingling, acute pains in the extremities or groin or pelvis, progressive weakness, pain when coughing or urinating, difficulty in walking and loss of bowel or bladder control.

Pain in the lower back may occur from an injury. However, it may be a symptom of a chronic issue like incorrect posture, scoliosis, spinal stenosis, ankylosing spondylitis, kidney infection, arthritis, fibromyalgia or spinal cancer. Furthermore, in women, lower back pain might occur due to ovarian cysts, uterine fibroids and ovarian cancer.

If a person is diagnosed with any of these infections, appropriate treatment of the underlying cause may resolve the back pain. One must consult a doctor immediately if back pain occurs with any of the above-mentioned symptoms.

Remedies for Sharp Back Pain

Sharp lower back pain is one of the common sources of discomfort for people which may develop after doing strenuous activities like weightlifting, making a quick twisting or jerking motion involving the back.

The severity of the back pain and the level of injury may decide the medications or the type of treatment. Muscle spasms usually respond well to rest and hence, avoiding physical activity for a few days may heal the affected muscles. However, after a few days of rest, engaging in some physical activity under the guidance of a physical therapist can help strengthen the muscles.

Applying hot or cold packs may help cure symptoms like swelling and pain. Using a cold pack for 20 minutes for the first few days and later on, using a heat pack for a similar duration may help relax the tense muscles and promote blood circulation.

Sciatica may improve without medical treatment within four to six weeks, but if the pain persists, one may have to consult a healthcare provider. A doctor can prescribe pain relievers and suggest physical therapy to treat sciatica pain. Surgical procedures may include lumbar decompression surgery and microdiscectomy for curing sciatica pain.

Some Over-the-Counter (OTC) pain relievers like ibuprofen, acetaminophen, steroid injections and other anti-inflammatory medications may help relieve pain. Most often, minor cases of sharp back pain resolve by themselves, but some may need doctor’s treatment and physical therapy to strengthen the muscles and prevent future injury.

Conclusion

There may be many causes of sharp back pain and most of them could be treated with simple remedies. But, if the home remedies do not work and the pain continues, one must consult a doctor and determine the exact cause. Doctors may prescribe the right medicines and suggest appropriate treatment to cure sharp back pain as per the causes and you can get back to your daytoday activities without any further delay.

Hp Fires Back, Sues Oracle For Hiring Hurd

Not so fast Oracle.

HP didn’t waste much time responding to Oracle’s Labor Day announcement that it had hired former HP (NYSE: HPQ) CEO Mark Hurdas its president. Tuesday, HP filed a civil complaint in California Superior Court aimed at preventing Hurd from working at Oracle (NASDAQ: ORCL).

The suit claims that by joining Oracle (NASDAQ: ORCL), Hurd will “inevitably” break the confidentiality agreement he signed to protect HP’s trade secrets.

HP also noted Hurd signed the company’s confidentiality agreement to protect trade secrets the past three years, most recently in February.

The news threatens to upend Oracle’s plans to give its new systems business a positive jolt with the hiring of enterprise heavyweight Hurd.

“There is no executive in the IT world with more relevant experience than Mark,” Oracle CEO Larry Ellison said in a statement on Hurd’s appointment. “Oracle’s future is engineering complete and integrated hardware and software systems for the enterprise.”

Barring a settlement, a case decided in HP’s favor could result in a judge enjoining Hurd from working at Oracle. In the interim, the case could drag on for months throwing Hurd’s effectiveness in doubt.

“I’m not a lawyer, but on the face of it, it seems HP’s complaint is valid primarily for one essential reason: Oracle’s purchase of Sun Microsystems,” Charles King, principal analyst with Pund-IT, told chúng tôi “If this had happened last year, pre-Sun, it wouldn’t have surprised me if Oracle could have effectively argued that it was a close strategic partner with HP and that Mark Hurd couldn’t tell them anything they didn’t already know.”

“But now that Oracle’s in the systems business, I’m not sure what Oracle or Hurd’s argument would be in terms of justifying any claim that his working there would not constitute a conflict,” King added.

The complaint said in part:

“Despite being paid millions of dollars in cash, stock and stock options in exchange for Hurd’s agreements to protect HP’s trade secrets and confidential information during his employment and following his departure from his positions at HP as Chairman of the Board, Chief Executive Officer, and President, HP is informed and believes and thereon alleges that Hurd has put HP’s most valuable trade secrets and confidential information in peril. “

“Hurd accepted positions with Oracle Corporation …, a competitor of HP, yesterday as its President and as a member of its Board of Directors. In his new positions, Hurd will be in a situation in which he cannot perform his duties for Oracle without necessarily using and disclosing HP’s trade secrets and confidential information to others.”

With the acquisition of Sun Microsystems earlier this year, Oracle is in the midst of a transition from software and services to a complete systems provider that competes with the likes of IBM and Dell as well as HP.

Hurd resigned from HPlast month as part of an agreement that paid him millions in severance following the revelation that he falsified expense reports to cover up a personal relationship with an HP contractor.

David Needle is the West Coast bureau chief at chúng tôi the news service of chúng tôi the network for technology professionals.

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