Transcriptie van de winst-conference call van Oil-Dri Q1 2025: recordverkopen, aandeel stijgt

{

protected override void OnCreate(Bundle savedInstanceState)

{

base.OnCreate(savedInstanceState);

Xamarin.Essentials.Platform.Init(this, savedInstanceState);

SetContentView(Resource.Layout.activity_main);

AndroidX.AppCompat.Widget.Toolbar toolbar = FindViewById(Resource.Id.toolbar);

SetSupportActionBar(toolbar);

FloatingActionButton fab = FindViewById(Resource.Id.fab);

fab.Click += FabOnClick;

ViewPager viewPager = FindViewById(Resource.Id.viewPager);

SetupViewPager(viewPager);

TabLayout tabLayout = FindViewById(Resource.Id.tabLayout);

tabLayout.SetupWithViewPager(viewPager);

}

private void SetupViewPager(ViewPager viewPager)

{

TabAdapter adapter = new TabAdapter(SupportFragmentManager);

adapter.AddFragment(new Fragment1(), "Tab 1");

adapter.AddFragment(new Fragment2(), "Tab 2");

adapter.AddFragment(new Fragment3(), "Tab 3");

viewPager.Adapter = adapter;

}

public override bool OnCreateOptionsMenu(IMenu menu)

{

MenuInflater.Inflate(Resource.Menu.menu_main, menu);

return true;

}

public override bool OnOptionsItemSelected(IMenuItem item)

{

int id = item.ItemId;

if (id == Resource.Id.action_settings)

{

return true;

}

return base.OnOptionsItemSelected(item);

}

private void FabOnClick(object sender, EventArgs eventArgs)

{

View view = (View) sender;

Snackbar.Make(view, "Replace with your own action", Snackbar.LengthLong)

.SetAction("Action", (View.IOnClickListener)null).Show();

}

public override void OnRequestPermissionsResult(int requestCode, string[] permissions, [GeneratedEnum] Android.Content.PM.Permission[] grantResults)

{

Xamarin.Essentials.Platform.OnRequestPermissionsResult(requestCode, permissions, grantResults);

base.OnRequestPermissionsResult(requestCode, permissions, grantResults);

}

}

}

End File# Jatinder303/Xamarin_Android_Tabbed_Application

using Android.App;

using Android.Content;

using Android.OS;

using Android.Runtime;

using Android.Util;

using Android.Views;

using Android.Widget;

using System;

using System.Collections.Generic;

using System.Linq;

using System.Text;

namespace Xamarin_Android_Tabbed_Application

{

public class Fragment2 : AndroidX.Fragment.App.Fragment

{

public override void OnCreate(Bundle savedInstanceState)

{

base.OnCreate(savedInstanceState);

// Create your fragment here

}

public override View OnCreateView(LayoutInflater inflater, ViewGroup container, Bundle savedInstanceState)

{

// Use this to return your custom view for this Fragment

// return inflater.Inflate(Resource.Layout.YourFragment, container, false);

return inflater.Inflate(Resource.Layout.fragment2, container, false);

}

}

}End Fileusing Android.App;

using Android.Content;

using Android.OS;

using Android.Runtime;

using Android.Views;

using Android.Widget;

using AndroidX.Fragment.App;

using Java.Lang;

using System;

using System.Collections.Generic;

using System.Linq;

using System.Text;

namespace Xamarin_Android_Tabbed_Application

{

public class TabAdapter : FragmentPagerAdapter

{

private List fragmentList = new List();

private List fragmentTitleList = new List();

public TabAdapter(FragmentManager fm) : base(fm)

{

}

public override int Count => fragmentList.Count;

public override AndroidX.Fragment.App.Fragment GetItem(int position)

{

return fragmentList[position];

}

public override ICharSequence GetPageTitleFormatted(int position)

{

return new Java.Lang.String(fragmentTitleList[position]);

}

public void AddFragment(AndroidX.Fragment.App.Fragment fragment, string title)

{

fragmentList.Add(fragment);

fragmentTitleList.Add(title">'sex', 'smoker', 'region'], drop_first=True)

# %%

# Correlation matrix

correlation_matrix = df.corr()

plt.figure(figsize=(12, 10))

sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')

plt.title('Correlation Matrix')

plt.show()

# %%

# Scatter plots

fig, axes = plt.subplots(2, 2, figsize=(15, 15))

sns.scatterplot(data=df, x='age', y='charges', ax=axes[0, 0])

sns.scatterplot(data=df, x='bmi', y='charges', ax=axes[0, 1])

sns.scatterplot(data=df, x='children', y='charges', ax=axes[1, 0])

sns.boxplot(data=df, x='smoker_yes', y='charges', ax=axes[1, 1])

plt.tight_layout()

plt.show()

# %%

# Prepare the data for modeling

X = df.drop('charges', axis=1)

y = df['charges']

# Split the data into training and testing sets

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Create and train the model

model = LinearRegression()

model.fit(X_train, y_train)

# Make predictions

y_pred = model.predict(X_test)

# Evaluate the model

mse = mean_squared_error(y_test, y_pred)

r2 = r2_score(y_test, y_pred)

print(f'Mean Squared Error: {mse}')

print(f'R-squared Score: {r2}')

# %%

# Plot actual vs predicted values

plt.figure(figsize=(10, 6))

plt.scatter(y_test, y_pred, alpha=0.5)

plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'r--', lw=2)

plt.xlabel('Actual Charges')

plt.ylabel('Predicted Charges')

plt.title('Actual vs Predicted Charges')

plt.show()

# %%

# Feature importance

feature_importance = pd.DataFrame({'feature': X.columns, 'importance': abs(model.coef_)})

feature_importance = feature_importance.sort_values('importance', ascending=False)

plt.figure(figsize=(10, 6))

sns.barplot(x='importance', y='feature', data=feature_importance)

plt.title('Feature Importance')

plt.show()

# %%

# Residual plot

residuals = y_test - y_pred

plt.figure(figsize=(10, 6))

sns.scatterplot(x=y_pred, y=residuals)

plt.axhline(y=0, color='r', linestyle='--')

plt.xlabel('Predicted Charges')

plt.ylabel('Residuals')

plt.title('Residual Plot')

plt.show()

# %%

# Distribution of residuals

plt.figure(figsize=(10, 6))

sns.histplot(residuals, kde=True)

plt.xlabel('Residuals')

plt.ylabel('Frequency')

plt.title('Distribution of Residuals')

plt.show()

# %%

# Q-Q plot

from scipy import stats

fig, ax = plt.subplots(figsize=(10, 6))

stats.probplot(residuals, dist="norm", plot=ax)

ax.set_title("Q-Q Plot")

plt.show()

End File# README.md

# Medical Insurance Cost Prediction

This project aims to predict medical insurance costs based on various factors such as age, BMI, number of children, smoking status, and region. We use a Linear Regression model to make these predictions.

## Dataset

The dataset used in this project is the Medical Cost Personal Datasets from Kaggle. It includes the following features:

- Age

- Sex

- BMI

- Number of children

- Smoking status

- Region

- Charges (target variable)

## Requirements

To run this project, you need the following Python libraries:

- pandas

- numpy

- matplotlib

- scikit-learn

- seaborn

You can install these libraries using pip: