Search Results

< Back Smartphone App for University Students An all-purpose app for Ashesi students. Project from 2017 Life at Ashesi University, like any university, can be overwhelming and disorganized. To streamline this experience, I suggest the development of a versatile mobile app that centralizes various essential services, thereby aiding in effective time management for students. The university offers a range of services including student support, counseling, and tutoring. However, accessing these services often proves to be a cumbersome and time-consuming process. In addition to these, many students are unaware of the contact details for on-campus emergency services and national emergency numbers in Ghana. In critical situations, this lack of information could lead to wastage of precious time. To tackle these issues, the proposed app would be a comprehensive solution. It would feature functionalities like accurate weather forecasts by integrating with the Accuweather website for Berekuso forecasts, a meal plan balance checker linked with the Ashesi meal plan webpage, and a digital menu for campus eateries like Akornor and Big Ben. Additionally, the app would include a directory of contact details for Ashesi’s various services and emergency services, with the added convenience of calling these contacts directly from the app. This integration would ensure that all necessary information and services are readily accessible to students, thereby enhancing their university experience and safety. Pseudocode 1. When the app is started the homepage is displayed. 2. The homepage displays titles “Meal Plan,” “Weather,” “Ashesi Services,” “Food” and “Emergency Services.” 3. If “Meal Plan” is selected, the webpage of the Ashesi Meal plan is displayed. 4. If “Weather” is selected, the webpage for accuweather (set for Berekuso) is displayed. 5. If “Food” is selected restaurants in Ashesi are displayed. 6. Select any restaurant and their menu shall be displayed. 7. If “Ashesi Services” is selected a list of Ashesi Services are displayed. 8. Select any service and their contact details is displayed for calling . 9. If “Emergency Services” is selected a list of Emergency Services are displayed. 10. Select any emergency service and their contact details is displayed for calling . Figure 1: Flowchart * Due to the senstivity of some information within the app, kindly request for access. Upon access being granted, the links below shall be temporarily activated. Download APK via Github: https://github.com/akweix/Ash-App Download Android App via Thunkabale: https://x.thunkable.com/copy/b63301e1a6082169dd0d9aa036ac119d Previous Next

< Back Alternative Data Regressor Framework: Flow Chart A framework for linear regression of alternative data against financial asset prices - the flow chart Previous Next

< Back Web-Scrapper V1: Web Application Web Application for web-scrapping news articles Full code here on Python Anywhere Previous Next

< Back Ghana Stock Exchange: Real-Time Prices Web App V1 A basic web-application to find real time summaries of stocks on Ghana's Stock Exchange (GSE) This Web-app is fully powered by GSE-API: Ghana Stock Exchange API found on http://dev.kwayisi.org/ . Github: https://github.com/akweix/GSE_price_finder Listed Companies and their Tickers on GSE Previous Next

< Back Sustainability Dimensions of Stocks on the SIX:Render 2 Quantitatively assessing Brundtland's Dimensions (1987). The case of the SIX Previous Next

< Back Cocoa Production: Ghana and Ivory Coast - Historic Trend Work in progress Previous Next

< Back Initial margin requirement for Derivative Trading A simplified VaR-based approach to calculate the initial margin requirement for Derivative Trading Previous Next

< Back Electricity Consumption as a proxy of production: Draft 1 Using publicly available data on Swiss Power Consumption, this exploration seeks to identify an association with power consumption and select firms output https://www.swissgrid.ch/en/home/operation/grid-data/current-data.html#wide-area-monitoring https://www.ewz.ch/en/about-ewz/newsroom/current-issues/electricity-shortage/city-zurich-energy-consumption.html https://data.stadt-zuerich.ch/dataset/ewz_stromabgabe_netzebenen_stadt_zuerich https://data.stadt-zuerich.ch/group/energie Work in Progress Previous Next

< Back Game Theory: Prisoner's Dilemma Strategies Tools Recreating and Simulating Robert Axelrod's 1980 Computer Tournament. Previous Next

< Back Dynamic View of Ghana's Unemployment Investigating the trend and segmentation of employment in Ghana Previous Next

< Back Dynamic view of Ghana's Insurance Industry Work in progress Previous Next

< Back Alternative Data Regressor: V1 A Python Program to attain a linear regression of some alternative data against financial asset prices . A CSV file is the input. The output is the regression results. The provided Python program is designed to process time series data from a CSV file and execute a series of analytical steps based on a predefined decision tree. Key functionalities include: Reading a CSV File : The user inputs the path to a CSV file, which the program reads into a DataFrame. Stationarity Testing : It tests the time series data for stationarity using the Augmented Dickey-Fuller test. Adjusting for Non-Stationarity : If the data is non-stationary, it applies a log transformation to stabilize the time series. Re-testing for Stationarity : After transformation, it retests the data for stationarity. Significance Testing : Conducts an Ordinary Least Squares (OLS) regression to test the significance of the relationship between the time series and a dependent variable. Model Development and Evaluation : If a significant relationship is found, the program proceeds to develop a baseline regression model, which is then refined and evaluated based on its R-squared value. Output : The program outputs the results of the stationarity tests, significance tests, and the R-squared value of the regression model. import pandas as pd import numpy as np from statsmodels.tsa.stattools import adfuller from statsmodels.regression.linear_model import OLS import statsmodels.api as sm from scipy import stats import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split from sklearn.metrics import r2_score def test_stationarity(timeseries): # Perform Dickey-Fuller test: dftest = adfuller(timeseries, autolag='AIC') return dftest[1] # p-value def adjust_non_stationarity(data): # Adjusting for non-stationarity (example: log transformation) return np.log(data) def significance_testing(X, y): # Perform significance testing (example: OLS regression) X = sm.add_constant(X) # adding a constant model = OLS(y, X).fit() return model.pvalues def main(): # Load data file_path = input("Enter the path to your CSV file: ") df = pd.read_csv(file_path) # Assuming the time series column is named 'timeseries' timeseries = df['timeseries'] # Step 1: Test for Stationarity if test_stationarity(timeseries) > 0.05: # Step 2: Adjust Data for Non-Stationarity timeseries = adjust_non_stationarity(timeseries) # Step 3: Re-test for Stationarity if test_stationarity(timeseries) > 0.05: print("Data is still non-stationary after transformation. Ending process.") return else: print("Data is stationary after transformation. Proceeding with analysis.") else: print("Data is stationary. Proceeding with analysis.") # Step 4: Significance Testing # Assuming another column 'dependent_var' as the dependent variable pvalues = significance_testing(df[['timeseries']], df['dependent_var']) if any(pval < 0.05 for pval in pvalues[1:]): # Ignoring the constant's p-value print("Significant correlation found. Proceeding to model development.") else: print("No significant correlation found. Ending process.") return # Steps 5, 6, 7: Develop, Refine, and Evaluate Regression Model # This is a simplified example using OLS regression X_train, X_test, y_train, y_test = train_test_split(df[['timeseries']], df['dependent_var'], test_size=0.2, random_state=0) model = OLS(y_train, sm.add_constant(X_train)).fit() predictions = model.predict(sm.add_constant(X_test)) print("Model R-squared:", r2_score(y_test, predictions)) # Step 8: Interpret the Regression Line # This step is more analytical and depends on the specific model and data # Step 9: Comparative Analysis if __name__ == "__main__": main() Previous Next