diff --git a/README.md b/README.md index d35a633..51b95a5 100644 --- a/README.md +++ b/README.md @@ -11,8 +11,12 @@ 3. Installere nødvendige biblioteker, med en av disse: - `pip3 install -r requirements.txt` - `pip install -r requirements.txt` + +- For å hente data er det kritisk med en API-KEY, i `resources/README.md` står det forklart hvordan denne kan lages, og lagres. -## Oversikt +- I samme README.md fil finnes informasjon om de ulike notebookene og deres funksjon. + +## Struktur Her kommer oversikt over strukturen i prosjektet: - `data` denne mappen inneholder output data - `docs` denne mappen inneholder dokumentasjon @@ -23,16 +27,22 @@ Her kommer oversikt over strukturen i prosjektet: Det kan leses mer om disse i deres tilhørende `README.md` filer. -### Mappe del 1 - -#### Vår visjon av oppgaven +### Vår visjon av oppgaven Vår visjon, og ønske om sluttprodukt, er en værapplikasjon med mange muligheter som: - Velge ønsket sted, å få relevant tidsdata for dagen. -- Velge ønsket tidsperiode, på inntil 7-dager, og sted for å få hisoriske værdata. +- Velge ønsket tidsperiode, på inntil 7-dager for det siste året, og sted for å få hisoriske værdata. - Velge ønsket tidsperiode, og 2 steder for å sammenligne værdata innenfor samme tidsperiode. -- Velge ønsket sted, og sammenligne to like lange tidsperioder fra ønsket år, enten samme tid eller to forsjellige tider. - Velge ønsket sted, å få en prediksjon på fremtidig vær. +### Oppgave - oversikt +- [Oppgave 1 - Utviklingsmiljø](#oppgave-1---sett-opp-utviklingsmiljø) +- [Oppgave 2 - Datainnsamling](#oppgave-2---datainnsamling) +- [Oppgave 3 - Databehandling](#oppgave-3---databehandling) +- [Oppgave 4 - Dataanalyse](#oppgave-4---dataanalyse) +- [Oppgave 5 - Visualisering](#oppgave-5---visualisering) +- [Oppgave 6 - Predektiv analyse](#oppgave-6---prediktiv-analyse) +- [Oppgave 7 - Refleksjonsnotat](#oppgave-7---refleksjonsnotat) + #### Oppgave 1 - Sett opp utviklingsmiljø I oppsettet av utviklingmsiljøet har vi fokusert på å innstallere relevante biblioteker, s.feks: - Numpy @@ -41,7 +51,7 @@ I oppsettet av utviklingmsiljøet har vi fokusert på å innstallere relevante b - Jupyper Notebook #### Oppgave 2 - Datainnsamling -For å finne data, startet vi å lete på Kaggle, og fant et interessant datasett, men som ikke passet helt til prosjektets behov. +For å finne data, startet vi å lete på [Kaggle](https://www.kaggle.com), og fant et interessant datasett, men som ikke passet helt til prosjektets behov. Da begynte vi å lete etter API-er, først på Metrologisk Institutt: - [Airqualityforecast](https://api.met.no/weatherapi/airqualityforecast/0.1/documentation) @@ -63,7 +73,7 @@ Ettersom ingen av de fra MET funket etter vårt ønske, søkte vi videre på net - [Statistic Historical Data](https://openweathermap.org/api/statistics-api): for å hente statistisk historisk data som kan brukes til regresjon. Den tar utganspunkt i all historisk data og oppsummerer det for hver dag i løpet av et år. ##### Henting av data -For å hente data fra OpenWeatherMap API-en har vi skrevet en funskjon som tar inn stedsnavn, startdato og sluttdato, den legger da ønskede verdier inn i url-en og requester for ønsket sted og tidsperiode, sammen med API-key som er lagret i en env-fil og importert. +For å hente data fra OpenWeatherMap API-en har vi skrevet en funskjon som tar inn stedsnavn, startdato og sluttdato, den legger da ønskede verdier inn i url-en og henter data for ønsket sted og tidsperiode, sammen med API-key som er lagret i en env-fil og importert. Funksjonen er lagt inn i Juputer notebook filen, som spør brukeren etter input. Når funksjonen er kjørt og vi får tilbake respons-koden '200', som betyr ok, vil det printes at dataen er hentet. Om koden ikke er '200' vil vi få en feilmelding, og koden printes. @@ -74,109 +84,113 @@ Funksjonen tar inn data og ønsket filnavn, og sjekker om vi har mappen 'data', Funksjonen returnerer en print setning når dataen er skrevet, og legger ved filpath - ##### Hente data fra fil For å hente data fra json-fil, bruker vi pandas sin innebygde funksjon _read_json_, for deretter å lagra dataene i en pandas dataframe. #### Oppgave 3 - Databehandling -Vi har hele tiden fokusert på å forstå dataen vi har, derfor har vi lagret den i en json fil for å lettere kunne lese ut hvilke verdier vi har, og hvilke vi kanskje ikke trenger. De kolonnene vi mener vi ikke trenger har vi da fjernet. Så har vi sjekket etter feil og mangler i dataen, både med 'NaN' verdier, manglende kolonner eller ekstremverdier. +Vi har hele tiden fokusert på å forstå dataen vi har, derfor har vi lagret den i en json fil for å lettere kunne lese ut hvilke verdier vi har, og hvilke vi kanskje ikke trenger. De kolonnene vi mener vi ikke trenger har vi da fjernet. Så har vi sjekket etter feil og mangler i dataen, både med "NaN" (Not a Number) verdier, manglende kolonner eller ekstremverdier. ##### Metoder for å identifisere og håndtere manglende data -Metoder vi har brukt  er for eksempel pd.json_normalize, df.drop_duplicates og df.drop(columns = «name»). Ved json.normalize har vi fått konvertert dataene våre til en tabell, DataFrame, fordi det er lettere å manipulere. Df.drop_duplicates bruker vi for å enkelt håndtere duplikatene i datasettet. Vi har også kolonner som inneholder informasjon som ikke er relevant til det vi ønsker å finne og da bruker vi df.drop(column= «name») og setter inn kolonnenavnet i parentes bak, eksempel: df = df.drop(columns = «base») eller df = df.drop(columns = «visability»). Denne metoden er nyttig for å rydde opp i datasettet og håndtere fjerning av kolonner som ikke er relevant, og dermed blir det mer oversiktlig og ryddig å jobbe med. +Metoder vi har brukt  er for eksempel `pd.json_normalize`, `df.drop_duplicates` og `df.drop(columns = «name»)`. Ved `json.normalize` har vi fått konvertert dataene våre til en tabell, DataFrame, fordi det er lettere å manipulere. `df.drop_duplicates` bruker vi for å enkelt håndtere duplikatene i datasettet. Vi har også kolonner som inneholder informasjon som ikke er relevant til det vi ønsker å finne og da bruker vi `df.drop(column= «name»)` og setter inn kolonnenavnet i parentes bak, eksempel: `df = df.drop(columns = «base»)` eller `df = df.drop(columns = «visability»)`. Denne metoden er nyttig for å rydde opp i datasettet og håndtere fjerning av kolonner som ikke er relevant, og dermed blir det mer oversiktlig og ryddig å jobbe med. -Vi har også brukt missingno.matrix for å visualisere hvilke kolonner som mangler data, før vi har brukt enten fillna(0) for å endre 'NaN' verider til 0, eller fillna('obj.ffill()) for å bruke forrige lagret data. +Vi har også brukt missingno.matrix for å visualisere hvilke kolonner som mangler data, før vi har brukt enten `fillna(0)` for å endre "NaN" verdier til 0, eller `fillna('obj.ffill())` for å bruke forrige lagret data. Noen steder har vi også brukt `interpolate(method='linear', limit_direction='both')` som "gjetter" en verdi basert på verdien før og etter, `limit_direction='both'` bidrar til at den ikke trenger verdier både ofran og bak for å gjette, men baserer seg bare på den ene verdien den eventuelt måtte ha. ##### List comprehensions -I den ene koden til statistic_data_notebook er et eksempel på hvor vi har brukt list comprehension for å manipulere data. Vi bruker den til å manipulere temperaturene til celsius og lagre det resultatet i en ny kolonne, temp.mean_celsius. Vi har gjort dette fordi den metoden er mer effektiv å bruke enn for eksempel en direkte for-løkke.  +I den ene koden til `statistic_data_notebook.ipynb` er et eksempel på hvor vi har brukt list comprehension for å manipulere data. Vi bruker den til å manipulere temperaturene til celsius og lagre det resultatet i en ny kolonne, `temp.mean_celsius`. Vi har gjort dette fordi den metoden er mer effektiv å bruke enn for eksempel en direkte for-løkke.  -Dette er også brukt i statistic_data_notebook for å lage en kolonne bestående av måned og dag. +Dette er også brukt i `statistic_data_notebook.ipynb` for å lage en kolonne bestående av måned og dag. ##### Pandas SQL vs tradisjonell Pandas Pandas-syntaks kan være noe kompleks og da kan man for eksempel med sqldf, bruke SQL-spørringer på Pandas DataFrames. Dette kan gi en enkel måte å filtrere, transformere og gruppere data på, på en mindre kompleks måte. SQL-spørringer kan også være enklere å lese og vedlikeholde enn Pandas-operasjoner, når man jobber med komplekse datasett. -##### Uregelmessigheter i dataene -Uregelmessigheter vi kan forvente å møte på er blant annet manglende verdier. For å håndtere disse kan vi bruke metoder som for eksempel fillna(), som fyller manglende verdier med en standardverdi. Eller så kan vi bruke dropna(), som fjerner radene med manglende verdi. Vi kan også møte på ufullstendige datoer eller datoer i ukjent format. Da kan vi bruke pd.to_datetime() for å sikre at datoene blir riktig konvertert til datetime format.  +Vi har brukt Pandas SQL spesielt i de ulike compare-notebookene, hvor det er lettere å hente ut data basert på de ulike stedene. Vi har også lagret SELECT-setningene i variabler, eks. for statistisk data som gjennomsnitt, max og min temperatur. Når vi senere plotter gjennomsnittene, kan vi bare hente ut de lagrede variablene tilhørende hvert sted. -Vi kan også møte ekstremverdier, som vi kan fjerne ved å sjekke om de er "uteliggere" ved å ligge mer enn tre standardavvik i fra gjennomsnittet. Da kan vi bruke verdien før med fillna('obj.ffill()') eller bruke interpolate linear metoden for å få den mest "smoothe" overgangen mellom manglende verdier. Da den "gjetter" seg frem til manglende verdier. +##### Uregelmessigheter i dataene +Uregelmessigheter vi kan forvente å møte på er blant annet manglende verdier. For å håndtere disse kan vi bruke metoder som for eksempel `fillna()`, som fyller manglende verdier med en standardverdi. Eller så kan vi bruke `dropna()`, som fjerner radene med manglende verdi. Vi kan også møte på ufullstendige datoer eller datoer i ukjent format. Da kan vi bruke `pd.to_datetime()` for å sikre at datoene blir riktig konvertert til datetime format.  +Vi kan også møte ekstremverdier, som vi kan fjerne ved å sjekke om de er "uteliggere" ved å ligge mer enn tre standardavvik i fra gjennomsnittet. Da kan vi bruke verdien før med `fillna('obj.ffill()')` eller bruke interpolate linear metoden for å få den mest "smoothe" overgangen mellom manglende verdier. Da den "gjetter" seg frem til manglende verdier. #### Oppgave 4 - Dataanalyse -#### Numpy og Pandas til beregninger -For eksempel i notebook_statistic_data har vi brukt Pandas til å beregne gjennomsnittstemperaturen for å få oversikt over den typiske temperaturen i den perioden. Så har vi temp.median også som gir den midterste verdien og er viktig for når det er skjevheter i dataene. For eksempel hvis det er dager med ekstremt høy eller ekstremt lav temperatur vil medianen hjelpe oss med å gi en mer riktig representativ temperatur. Disse statistiske målene hjelper med å få innsikt i datasettet. Gjennomsnittet gir en generell indikasjon på temperaturen, medianen beskytter mot ekstreme verdier, og standardavviket forteller oss om variasjonen i dataene. +##### Numpy og Pandas til beregninger +For eksempel i `notebook_statistic_data.ipynb` har vi brukt Pandas til å beregne gjennomsnittstemperaturen for å få oversikt over den typiske temperaturen i den perioden. Så har vi temp.median også som gir den midterste verdien og er viktig for når det er skjevheter i dataene. For eksempel hvis det er dager med ekstremt høy eller ekstremt lav temperatur vil medianen hjelpe oss med å gi en mer riktig representativ temperatur. Disse statistiske målene hjelper med å få innsikt i datasettet. Gjennomsnittet gir en generell indikasjon på temperaturen, medianen beskytter mot ekstreme verdier, og standardavviket forteller oss om variasjonen i dataene. -#### Kan du gi et eksempel på hvordan du vil implementere en enkel statistisk analyse for å undersøke sammenhengen mellom to variabler i datasettet? -Et eksempel på hvordan vi har implementert en enkel statistisk analyse for å undersøke sammenhengen mellom to variabler i datasettet er blant annet scatterdiagram. Vi har en linje som viser gjennomsnittstemperaturen og punkter som viser den faktiske temperaturene den dagen. Dette viser godt sammenheng og visualiserer sammenhengen mellom de to variablene på en oversiktelig måte. Scatterdiagramet gir innsikt i detaljene bak linjen til gjennomsnittet. +##### Kan du gi et eksempel på hvordan du vil implementere en enkel statistisk analyse for å undersøke sammenhengen mellom to variabler i datasettet? +Et eksempel på hvordan vi har implementert en enkel statistisk analyse for å undersøke sammenhengen mellom to variabler i datasettet er blant annet *scatterdiagram*. Vi har en linje som viser gjennomsnittstemperaturen og punkter som viser den faktiske temperaturene den dagen. Dette viser godt sammenheng og visualiserer sammenhengen mellom de to variablene på en oversiktelig måte. Scatterdiagramet gir innsikt i detaljene bak linjen til gjennomsnittet. -#### Håndtering av skjevheter -Vi har møtt på noen skjevheter som for eksempel at temperaturen har vært urealistisk i perioder, for å rydde opp i dette har vi sjekket uteliggere, altså sjekket om verdiene ligger mer enn 3 standardavvik fra gjennomsnittet. Om standardavviket er høyere enn 3 byttet ut disse verdiene med mer realistisk verdi basert på de andre verdiene. +##### Håndtering av skjevheter +Vi har møtt på noen skjevheter som for eksempel at temperaturen har vært urealistisk i perioder, for å rydde opp i dette har vi sjekket uteliggere, altså sjekket om verdiene ligger mer enn 3 standardavvik fra gjennomsnittet. Om en verdi ligger mer enn 3 standardavvik over eller under gjennomsnittet, har vi brukt `interpolate(method='linear', limit_direction='both')`, som "gjetter" verdien basert på veriden før og etter. #### Visualiseringer av funnene våre -Vi har laget mange grafer som viser både temperatur, nedbør i både regn og snø, og vindmålinger for å visuelt formidle funnene våre. Disse grafene kan du finne i våre notebooks å se visuelt selv, men vil her gi mer detaljert dokumentasjon for visualiserings valgene våre. +Vi har laget mange grafer som viser både temperatur, nedbør i både regn og snø, og vindmålinger for å visuelt formidle funnene våre. Disse grafene kan du finne i våre notebooks å se visuelt selv, men vil her gi mer detaljert dokumentasjon for visualiserings valgene våre. + +I dette prosjektet har vi valgt å bruke ulike typer diagrammer for å visualisere værdata, spesielt temperatur og nedbør. Notebooksene våre inneholder ulike egenskaper, blant annet værdata for ulike tidsperioder. Vi har time for time av en valgt dag og sted, periode over flere dager (dager og sted blir valgt av bruker) og for et år (her velger bruker kun sted). Målet har vært å gjøre dataene forståelige og lett sammenlignbare for brukeren, samtidig som det gir en nøyaktig og tydelig fremstilling. + +Ettersom temperatur er en kontinuerlig variabel som endrer seg gradvis over tid har vi valgt å bruke linjediagram for å visualisere temperaturdataen. Vi gikk for dette type diagrammet for temperatur fordi det viser utviklingen på en jevn og oversiktelig måte og gjør det lett å følge temperaturens stigning og fall. I tillegg, spesielt på diagrammet for et års periode, kan det gi brukeren en god forståelse av trender og sesongvariasjoner da x-aksen vår er månedene i året. Linjediagrammet gjør det også lettere enn for eksempel søylediagram, å lese mønstre og identifisere tidsperioder med veldig høy eller lav temperatur. + +Søylediagram derimot følte vi var det beste alternative for visualisering av nedbør av snø og regn. Siden nedbør måles i mengde per tidsenhet (millimeter) gjør søylediagrammet det lett å sammenligne hvor mye nedbør som har falt i ulike perioder. Vi har også ulike farger på snø og regn for å enkelt kunne skille de. Søylediagrammet gjør det også lett å identifisere perioder med mye nedbør eller ingen nedbør basert på om søylen er høy eller lav/ikke eksisterende. -I dette prosjektet har vi valgt å bruke ulike typer diagrammer for å visualisere værdata, spesielt temperatur og nedbør. Notebooksene våre inneholder ulike egenskaper, blant annet værdata for ulike tidsperioder. Vi har time for time av en valgt dag og sted, periode over flere dager (dager og sted blir valgt av bruker) og for et år (her velger bruker kun sted). Målet har vært å gjøre dataene forståelige og lett sammenlignbare for brukeren, samtidig som det gir en nøyaktig og tydelig fremstilling. -Ettersom temperatur er en kontinuerlig variabel som endrer seg gradvis over tid har vi valgt å bruke linjediagram for å visualisere temperaturdataen. Vi gikk for dette type diagrammet for temperatur fordi det viser utviklingen på en jevn og oversiktelig måte og gjør det lett å følge temperaturens stigning og fall. I tillegg, spesielt på diagrammet for et års periode, kan det gi brukeren en god forståelse av trender og sesongvariasjoner da x-aksen vår er månedene i året. Linjediagrammet gjør det også lettere enn for eksempel søylediagram, å lese mønstre og identifisere tidsperioder med veldig høy eller lav temperatur. -Søylediagram derimot følte vi var det beste alternative for visualisering av nedbør av snø og regn. Siden nedbør måles i mengde per tidsenhet (millimeter) gjør søylediagrammet det lett å sammenligne hvor mye nedbør som har falt i ulike perioder. Vi har også ulike farger på snø og regn for å enkelt kunne skille de. Søylediagrammet gjør det også lett å identifisere perioder med mye nedbør eller ingen nedbør basert på om søylen er høy eller lav/ikke eksisterende. Scatterplot er et annet diagram vi har brukt. Dette mente vi var best egnet blant annet til når vi skulle visualisere temperaturverdiene for en dag opp mot gjennomsnittstemperaturen den dagen. Den gir en tydelig og enkel oversikt med punkter for temperaturen per time for en dag og bruker kan lett lese av diagrammet om punktene er over eller under gjennomsnittsstreken. Diagrammet gir en tydelig fremstilling av variasjonen i temperaturen i løpet av dagen og hvordan enkelttimer skiller seg fra resten og gjennomsnittet. Scatterplot egner seg godt her fordi det viser detaljene i hver enkelt måling, samtidig som det gir rom for å indikere trender på en presis måte uten å koble punktene sammen. -Vi har også lagt til en notebook med interaktive visualiseringer. Denne notebooken har nokså lik kode som notebook_one_day_data, men på den er det ikke interaktivt, noe som notebook_interactive_data tilbyr. Dette for at brukeren selv kan få bestemme visualiseringen de selv synes er best og kan få endre litt på effekter som for eksempel farge, scatterplotsstørrelse, om rutenett og gjennomsnitt er med eller ikke. +Vi har også lagt til en notebook med interaktive visualiseringer. Denne notebooken har nokså lik kode som `notebook_one_day_data`, men på den er det ikke interaktivt, noe som `notebook_interactive_data` tilbyr. Dette for at brukeren selv kan få bestemme visualiseringen de selv synes er best og kan få endre litt på effekter som for eksempel farge, scatterplotsstørrelse, om rutenett og gjennomsnitt er med eller ikke. #### Oppgave 5 - Visualisering -#### Hvilke spesifikke typer visualiseringer planlegger du å lage for å representere eksempelvis endringer i luftkvalitet og temperaturdata, og hvorfor valgte du disse? -Vi har laget visualiseringer for temperatur og andre værforhold både for dagens dato, alle timene i en dag og sted brukeren selv velger, en kort periode og sted som brukeren selv velger og årlig for et sted brukeren selv velger. Det årlige dataene er basert på alle tidligere år sammenlagt. Vi valgte akkurat disse fordi det gir brukeren flere muligheter å sjekke ulike perioder og steder. Vi valgte nedbør, vind og temperatur fordi det er de værforholdene vi mener er mest interessante å vite. I notebook_current_data som viser data fra dagen idag har vi ikke tatt en graf men heller en kolonne med informasjon. Her har vi i tillegg til værdataene lagt til solnedgang og soloppgang fordi vi tenker at det kan være noe brukeren synes er relevant når spør om dagens data. +##### Hvilke spesifikke typer visualiseringer planlegger du å lage for å representere eksempelvis endringer i luftkvalitet og temperaturdata, og hvorfor valgte du disse? +Vi har laget visualiseringer for temperatur og andre værforhold både for dagens dato, alle timene i en dag og sted brukeren selv velger, en kort periode og sted som brukeren selv velger og årlig for et sted brukeren selv velger. Det årlige dataene er basert på alle tidligere år sammenlagt. Vi valgte akkurat disse fordi det gir brukeren flere muligheter å sjekke ulike perioder og steder. Vi valgte nedbør, vind og temperatur fordi det er de værforholdene vi mener er mest interessante å vite. I `notebook_current_data.ipynb` som viser data fra dagen i dag har vi ikke tatt en graf, men heller en kolonne med informasjon. Her har vi i tillegg til værdataene lagt til solnedgang og soloppgang fordi vi tenker at det kan være noe brukeren synes er relevant når spør om dagens data. -#### Hvordan kan Matplotlib brukes til å forbedre forståelsen av de analyserte dataene? -Vi har blant annet brukt matplotlib.dates (as mdates) matplotlib.pyplot (as plt) for å få et mer oversiktelig diagram av de analyserte dataene. Funksjoner vi har brukt er for eksempel plt.gca for å formatere aksene sin datetime til månedlig. Denne har vært fin å bruke til oversiktelig tilpassing av aksene. Vi har også flere steder brukt plt.title, plt.grid() og plt.legend for å få mer forkalringer og oversiktelighet på diagrammene våre og for å få de til å se like ut slik at det er enkelt for brukeren å sette seg inn i grafene uansett hvilken notebook. Andre eksempler på matplotlib vi har brukt er plt.savefig for å lagre til en fil som PNG, slik man enkelt kan finne tilbake til det. +##### Hvordan kan Matplotlib brukes til å forbedre forståelsen av de analyserte dataene? +Vi har blant annet brukt` matplotlib.dates (as mdates)` og `matplotlib.pyplot (as plt)` for å få et mer oversiktelig diagram av de analyserte dataene. Funksjoner vi har brukt er for eksempel `plt.gca` for å formatere aksene sin datetime til månedlig. Denne har vært fin å bruke til oversiktelig tilpassing av aksene. Vi har også flere steder brukt `plt.title`, `plt.grid()` og `plt.legend` for å få mer forklaringer og oversiktelighet på diagrammene våre og for å få de til å se like ut slik at det er enkelt for brukeren å sette seg inn i grafene uansett hvilken notebook. Andre eksempler på matplotlib vi har brukt er `plt.savefig` for å lagre til en fil som PNG, slik man enkelt kan finne tilbake til det. -#### Hvordan vil du håndtere og visualisere manglende data i grafene dine for å sikre at de fortsatt er informative? +##### Hvordan vil du håndtere og visualisere manglende data i grafene dine for å sikre at de fortsatt er informative? Da vil vi at den manglende dataen blir byttet ut med data som er hentet fra tidspunktene rundt slik den manglende dataen får en verdi som passer med de andre. Vi har også for eksempel laget søylediagram som viser manglende data for feks. snø og regn ettersom det ikke alltid snør og regner også videre laget et nytt diagram hvor de manglende dataene for snø og regn er byttet med 0. -#### Kan du beskrive prosessen for å lage interaktive visualiseringer med Widgets, Plotly eller Bokeh, og hvilke fordeler dette kan gi i forhold til statiske visualiseringer? + +##### Kan du beskrive prosessen for å lage interaktive visualiseringer med Widgets, Plotly eller Bokeh, og hvilke fordeler dette kan gi i forhold til statiske visualiseringer? Man må ha et datasett klart, for eksempel et DataFrame i Python. Deretter velge en type graf, her kan man gjerne bruke plotly som støtter mange ulike visualiseringer som for eksempel linjediagram eller stolpediagram. Videre bestemmer man x og y aksen. Dersom man bruker plotly vil det gi noen automatiske funksjoner som blant annet zooming og hover-effekten. Widgets er interaktive kontroller som dropdown-menyer, sliders og tekstfelt. Når du kombinerer disse med visualiseringene (f.eks. i Jupyter Notebooks), kan du lage dashbord hvor brukeren selv styrer hvilke data som vises, uten å måtte endre koden. Bokeh funker på samme måte som plotly men er mer rettet mot web-applikasjoner og er litt mer teknisk å sette opp enn plotly. I forhold til statistiske visualiseringer vil disse visualiseringene ha mer effekter som for eksempel zooming, dynamiske menyer (dropdowns, sliders) og enkelt å dele i web (HTML eksport). -#### Hvordan vil du evaluere effektiviteten av visualiseringene dine i å formidle de viktigste funnene fra dataanalysen til et bredere publikum? +##### Hvordan vil du evaluere effektiviteten av visualiseringene dine i å formidle de viktigste funnene fra dataanalysen til et bredere publikum? Vi mener effektiviteten i visualiseringene våre er svært god fordi grafene og søylediagramene er lett lesbare og vi har kodet ut kolonner vi ikke mener har data med viktig informasjon. Grafene har også fargekoder og kombinasjoner for de ulike værforholdene noe som gir en god visuell oversikt. -#### Opggave 6 - Prediktiv analyse +#### Oppgave 6 - Prediktiv analyse -#### Lag minst tre forskjellige typer visualiseringer (f.eks. linjediagrammer, søylediagrammer og scatterplots) for å representere endringer i eksempelvis luftkvalitet og temperaturdata over tid. Forklar valget av visualiseringstype for hver graf. -Vi har laget linjediagrammer, søylediagrammer og scatterplots. Alle tre typer kan man finne i notebook_one_day_data.ipynb, i notebook_one_week.ipynb kan man finne søylediagram og linjediagram. I notebook_statistic_data.ipynb kan man finne linjediagram og søylediagram. +##### Lag minst tre forskjellige typer visualiseringer (f.eks. linjediagrammer, søylediagrammer og scatterplots) for å representere endringer i eksempelvis luftkvalitet og temperaturdata over tid. Forklar valget av visualiseringstype for hver graf. +Vi har laget linjediagrammer, søylediagrammer og scatterplots. Alle tre typer kan man finne i n`otebook_one_day_data.ipynb`, i `notebook_one_week.ipynb` kan man finne søylediagram og linjediagram. I `notebook_statistic_data.ipynb` kan man finne linjediagram og søylediagram. Linjediagram ble brukt i de fleste notebooks fordi det passer godt til å vise utvikling over tid, spesielt for temperaturen. Søylediagram ble brukt fordi det gjør det enkelt å visuelt se hvor mye feks. regn eller snø det var i den perioden. Scatterplots ble brukt for å visuelt vise hvor mye temperaturen hver time er over eller under gjennomsnittet for dagens temperatur. -#### Implementer visualiseringer ved hjelp av Matplotlib og Seaborn. Inkluder tilpassede akser, titler, og fargepaletter for å forbedre lesbarheten og estetikk. +##### Implementer visualiseringer ved hjelp av Matplotlib og Seaborn. Inkluder tilpassede akser, titler, og fargepaletter for å forbedre lesbarheten og estetikk. Dette har vi brukt Matplotlib til og visualiseringene kan du finne i notebooksene. -#### Demonstrer hvordan manglende data håndteres i visualiseringene. Lag en graf som viser hvordan manglende verdier påvirker datatrender, og diskuter hvordan dette kan påvirke tolkningen av dataene. -Visualiseringen finner man i notebooksene. Vi har identifisert manglende data og fylt NaN med 0 der det er nødvendig og gitt en ny visualisering der dette er fylt inn. Manglende verdier kan påvirke datatrender og tolkning blant annet ved at dersom dataen interpoleres automatisk kan man tolke trender som ikke finnes. Eller så kan manglende verdier føre til at viktige mønstre blir skjult. Det kan også hende at dette da fører til at en analytiker har en modell eller beslutning basert på trender som ikke har virkelig data. +##### Demonstrer hvordan manglende data håndteres i visualiseringene. Lag en graf som viser hvordan manglende verdier påvirker datatrender, og diskuter hvordan dette kan påvirke tolkningen av dataene. +Visualiseringen finner man i notebook-ene. Vi har identifisert manglende data og fylt NaN med 0 der det er nødvendig og gitt en ny visualisering der dette er fylt inn. Manglende verdier kan påvirke datatrender og tolkning blant annet ved at dersom dataen interpoleres automatisk kan man tolke trender som ikke finnes. Eller så kan manglende verdier føre til at viktige mønstre blir skjult. Det kan også hende at dette da fører til at en analytiker har en modell eller beslutning basert på trender som ikke har virkelig data. -#### Skriv en kort evaluering av de utviklede visualiseringene. Diskuter hvilke visualiseringer som var mest effektive for å formidle informasjon, og hvorfor. Reflekter over tilbakemeldinger fra medstudenter eller veileder. +##### Skriv en kort evaluering av de utviklede visualiseringene. Diskuter hvilke visualiseringer som var mest effektive for å formidle informasjon, og hvorfor. Reflekter over tilbakemeldinger fra medstudenter eller veileder. Linjegrafene viste seg å være de mest effektive når det gjaldt å formidle utvikling over tid, spesielt for å identifisere trender og mønstre i dataene. Kombinasjonen av tydelige akser, fargebruk og merking av nøkkelpunkter gjorde det enkelt å tolke informasjonen raskt. I tillegg fungerte kombinasjonsgrafene (linjediagram og stolpediagram) godt for å sammenligne flere dataserier parallelt, som f.eks. temperatur og nedbør. #### Oppgave 7 - Refleksjonsnotat -Det jeg har lært om datainnsamling, databehandling og dataanalyse, visualisering gjennom dette prosjektet er at det krever en del arbeid og kan være frustrerende, men når man får det til så er det en utrolig mestringsfølelse og en veldig oversiktlig og informativ representasjon av datainnsamlingene. Visualiseringen av dataen var mest givende fordi vi fikk tydelig se at databehandlingen og datarenskingen ga resultater og et fint layout. +Det vi har lært om datainnsamling, databehandling og dataanalyse, visualisering gjennom dette prosjektet er at det krever en del arbeid og kan være frustrerende, men når man får det til så er det en utrolig mestringsfølelse og en veldig oversiktlig og informativ representasjon av datainnsamlingene. Visualiseringen av dataen var mest givende fordi vi fikk tydelig se at databehandlingen og datarenskingen ga resultater og et fint layout. -Jeg har hatt noe koding med Pythons-bibliotekene Pandas, NumPy og Matplotlib fra emne TDT4111 forrige semester. Men gjennom dette emne og denne oppgaven har jeg blitt tryggere på alle tre, spesielt Pandas. Jeg har også blitt kjent med et nytt bibliotek, Scikit-learn og fått nye ferdigheter til hvordan å bruke dette til å gjøre lineær regresjon. +Vi har hatt noe koding med Pythons-bibliotekene Pandas, NumPy og Matplotlib fra emne TDT4111 forrige semester. Men gjennom dette emne og denne oppgaven har vi blitt tryggere på alle tre, spesielt Pandas. Vi har også blitt kjent med et nytt bibliotek, Scikit-learn og fått nye ferdigheter til hvordan å bruke dette til å gjøre lineær regresjon. -Når vi først skulle komme i gang med prosjektet møtte vi på en del hindringer og komplikasjoner ved GitHub og API. Jeg hadde laget mappen min for prosjektet i OneDrive mappen min og begynt å jobbe der og da når jeg skulle commite og pulle og slikt så gikk ikke dette og jeg fikk flere feilmeldinger. Fikk feilmeldinger om API og at mappen var feil, vi fikk laget en ny API og fikk fortsatt feilmelding. Etter litt frem og tilbake så prøvde vi å flytte mappen fra OneDrive til direkte på datamaskinen og da løste det seg. Ellers har det noen ganger dukket opp feilmeldinger når jeg har manglet biblioteker eller de har trengt å oppdatere seg, men disse små hindrene løste vi fort. +Når vi først skulle komme i gang med prosjektet møtte vi på en del hindringer og komplikasjoner ved GitHub og API. Vi støtte på et problem etter å ha klonet repoet inn i en mappe i OneDrive, når vi skulle begynne å pushe og pulle. Etter flere feilmeldinger og forsøk på å løse dette, prøvde vi å flytte mappen fra OneDrive til direkte på datamaskinen og da løste det seg. Ellers har det noen ganger dukket opp feilmeldinger når vi har manglet biblioteker eller de har trengt å oppdatere seg, men disse små hindrene løste vi fort ved å installere `requirements.txt` på nytt. -Jeg synes samarbeidet i gruppen har gått bra. Når jeg har møtt på hinder har hun vært veldig flink til å hjelpe meg og løse de sammen med meg. Vi har møttes jevnt og jobbet litt sammen og fordelt oppgaver mellom oss som skal jobbes med frem til vi møtes igjen. Ettersom jeg hadde noen vanskeligheter med GitHub iblant har vi løst det med at jeg har sendt arbeidet mitt til henne på mail og hun har lagt det inn i GitHub. +Sammarbeidet i gruppen har gått bra. Når vi har møtt på hindre har vi vært flinke til å hjelpe hverandre og løse de sammen. Vi har møttes jevnlig, jobbet sammen og fordelt oppgaver oss i mellom frem til neste møte. Hanne har hatt noen problemer med GitHub, så noen av løsningene har vært å sende tekst til README.md filer på mail. Ikke en optimal løsning, men vi fikk det bedre til tilslutt med en del pull og merge request. -Kvaliteten på dataene våre mener jeg er gode ettersom vi har en API som man trenger å lage bruker for (gratis) og har utrolig mye data for mange mange år og mange steder. Jeg mener dette styrker troverdigheten og legitimiteten til dataene våre. Vi har fått laget mange diagrammer og mange Jupyter Notebooks som gjør at brukeren kan velge fra forskjellige perioder og steder de ønsker informasjon om værforhold fra. Grafene er lett lesbare og har realistisk data noe jeg mener styrker kvaliteten på dataene våre. Ettersom dataene fra API-en vår er i kelvin og ikke celsius har det noen steder vært litt komplikasjoner med riktig temperatur, noe som gjerne svekker kvaliteten litt. +Kvaliteten på dataene våre mener vi er gode ettersom vi har en API som man trenger å lage bruker for (gratis) og har utrolig mye data for mange mange år og mange steder. Vi mener dette styrker troverdigheten og legitimiteten til dataene våre. Vi har fått laget mange diagrammer og mange Jupyter Notebooks som gjør at brukeren kan velge fra forskjellige perioder og steder de ønsker informasjon om værforhold fra. Grafene er lett lesbare og har realistisk data noe vi mener styrker kvaliteten på dataene våre. Ettersom dataene fra API-en vår er i kelvin og ikke celsius har det noen steder vært litt komplikasjoner med riktig temperatur, noe som gjerne svekker kvaliteten litt. For videre forskning kunne vi utviklet kodene våre til å gi brukeren valget om å velge byer utenfor Norge. Vi har i dette prosjektet begrenset til Norge, så å ha muligheten til å sjekke byer i hele verden ville vært et bra og naturlig steg videre i forskningen. -Det jeg mener er noen av de viktigste læringspunktene er at jeg har fått blitt bedre kjent med biblioteker og fått god øving på å programmere og jobbe i gruppe. Med store datamengder av været kan man sammenligne værforhold i dag og mange år tilbake og se på forskjellene. Da kan man prøve å se det i miljøaspekt og det kunne også vært interessante forhold å se på videre. Våre værdata i sammenheng med miljø og klima utviklingen gjennom årene. Andre viktige læringspunkter jeg har fått bedre forståelse for er iteratorer og list-comprehensions, enhetstesting, kunne håndtere data lagring, feil og filbehandlinger. +Det vi mener er noen av de viktigste læringspunktene er at vi har fått blitt bedre kjent med biblioteker og fått god øving på å programmere og jobbe i gruppe. Med store datamengder av været kan man sammenligne værforhold i dag og mange år tilbake og se på forskjellene. Da kan man prøve å se det i miljøaspekt og det kunne også vært interessante forhold å se på videre. Våre værdata i sammenheng med miljø og klima utviklingen gjennom årene. Andre viktige læringspunkter vi har fått bedre forståelse for er iteratorer og list-comprehensions, enhetstesting, kunne håndtere data lagring, feil og filbehandlinger. -Jeg tror at å ha erfaringene jeg har lært om Pythons generelt, VS-Code, Jupyter Notebooks vil komme godt med i arbeidslivet dersom man finner en jobb innenfor programmerings område. Samtidig som de grunnleggende programmeringsferdighetene vil komme godt med, vil det at vi har jobbet mye med statistikk og grafer gjøre at vi har en bedre forståelse av de områdene noe som man vil trenge videre i studie og i en eventuell jobb. Det at vi har jobbet i grupper har også en positiv påvirkning ved at vi lærer og samarbeidet og kommunisere med andre. \ No newline at end of file +Vi tror at å ha erfaringene vi har lært om Pythons generelt, VS-Code, Jupyter Notebooks vil komme godt med i arbeidslivet dersom man finner en jobb innenfor programmerings område. Samtidig som de grunnleggende programmeringsferdighetene vil komme godt med, vil det at vi har jobbet mye med statistikk og grafer gjøre at vi har en bedre forståelse av de områdene noe som man vil trenge videre i studie og i en eventuell jobb. Det at vi har jobbet i grupper har også en positiv påvirkning ved at vi lærer og samarbeidet og kommunisere med andre. \ No newline at end of file diff --git a/data/README.md b/data/README.md index 2721585..29c23c4 100644 --- a/data/README.md +++ b/data/README.md @@ -4,8 +4,13 @@ Her vil det opprettes ulike mapper som et resultat av dataene som lagres gjennom Funksjonen er bygd slik at den først sjekker om det eksisterer en mappe, før den eventuelt lager. Alle mapper som starter med output (altså output data) er lagt til i `.gitignore`. Dette for å ikke laste opp masse unødvendig til github, men også for at brukere ikke 'deler' data. Mine kjøringer vil være mine, og dine vil kun vises hos deg. +Det er også en test-mappe, her har vi lagret noe data hentet med API-en, som inneholder feil og mangler. Denne vil bli brukt i `notebook_regression.ipynb` for å vise at funksjonene våre for renskning fungerer, før dataen blir splittet og trener opp en regresjonsmodell. + Dette er eksempel på noen av mappene: +- `test` inneholder forhåndslagret data for bruk i `notebook_regression.ipynb` - `output_current_data` lagrer dataen for ønsket sted, kjørt fra `notebook_current_data.ipynb` - `output_fig` lagrer grafer, kjørt fra `notebook_statistic_data.ipynb` - `output_record` lagrer rekord data fra ønsket sted, kjørt fra `notebook_statistic_data.ipynb` -- `output_statistikk` lagrer dataen for ønsket sted, kjørt fra`notebook_statistic_data.ipynb` \ No newline at end of file +- `output_statistikk` lagrer dataen for ønsket sted, kjørt fra`notebook_statistic_data.ipynb` +- `output_sammenligning_dag` lagrer data for to ønsket steder for dags sammenligning, kjørt fra `notebook_compare_one_day_data.ipynb` +- `output_fig_sammenligning` lagrer sammenligningsgrafer, kjørt fra de ulike 'compare' notebookene 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}, + "wind": { + "speed": 0.89, + "deg": 158, + "gust": 2.68 + }, + "clouds": { + "all": 68 + }, + "weather": [ + { + "id": 803, + "main": "Clouds", + "description": "broken clouds", + "icon": "04n" + } + ] + } + ] +} \ No newline at end of file diff --git a/notebooks/README.md b/notebooks/README.md index 23f68fb..45bd46a 100644 --- a/notebooks/README.md +++ b/notebooks/README.md @@ -10,5 +10,13 @@ Her finnes informasjon om de ulike notebookene og deres innhold. Denne notebooken henter data fra ønsket periode (inntil 7-dager) og sted, skriver til fil. Visualiserer manglende verdier, retter opp manglende verdier, og visualisere og lagrer data fra plot. - [Statistic year data](notebook_statistic_data.ipynb) Denne notebooken henter data fra en API som samler alle historiske data for ønsket sted, å regner ut statistiske verdier for alle dagene i året. Vi fjerner uønskede kolonner, utelukker ekstremverdier og visualiserer data gjennom plotter. +- [Compare one day data](notebook_compare_one_day_data.ipynb) + Denne notebooken henter data for ønsket dag fra to steder, og sammenglinger verdiene. Dataen fra begge stedene lagres i samme json-fil, og hentes ut ved hjelp av Pandas SQL. +- [Compare one week data](notebook_compare_one_week_data.ipynb) + Denne notebooken henter data for ønsket periode (inntil 7-dager) fra to steder, og sammenglinger verdiene. Dataen fra begge stedene lagres i samme json-fil, og hentes ut ved hjelp av Pandas SQL. +- [Compare statistic data](notebook_compare_statistic_data.ipynb) + Denne notebooken henter data fra en API som samler alle historiske data for to ønskede steder, og sammenglinger verdiene. Dataen fra begge stedene lagres i samme json-fil, og hentes ut ved hjelp av Pandas SQL. +- [Regression](notebook_regression.ipynb) + Denne notebooken henter data fra et test-fil, hvor vi vet det er mangler, og kjører rensking av dataen før den splittes og trener opp en regresjonsmodell. Du kan også legge inn ulike verdier til variablene for å se regresjonen i live-action. - [Test notebook](test_notebook.ipynb) - Dette er bare en test notebook, for å se om venv funker og det å importere funksjoner fra packager. \ No newline at end of file + Dette er bare en test notebook, for å se om venv funker og det å importere funksjoner fra packager. diff --git a/notebooks/notebook_compare_one_week_data.ipynb b/notebooks/notebook_compare_one_week_data.ipynb index aa1c3ee..2b83191 100644 --- a/notebooks/notebook_compare_one_week_data.ipynb +++ b/notebooks/notebook_compare_one_week_data.ipynb @@ -5,7 +5,9 @@ "id": "06c938cd", "metadata": {}, "source": [ - "# info" + "# Notebook - Compare one week data\n", + "\n", + "Denne notebooken henter data fra to ønskede steder i Norge, etter ønsket tidspreiode (intill 7-dager) og sammenligner dataen ved hjelp av visualisering i matplotlib av temperatur og nedbør." ] }, { @@ -145,7 +147,7 @@ "Skriv inn navn for til filen du vil lagre med dataen.\n", "\n", "Eks. test\n", - "Da vil filen lagres som data_**test**.json, i mappen \"../data/output_sammenligning_uke/data_{filnavn}.json\"" + "Da vil filen lagres som data_**test**.json, i mappen `../data/output_sammenligning_uke/data_{filnavn}.json`" ] }, { diff --git a/notebooks/notebook_compare_statistic_data.ipynb b/notebooks/notebook_compare_statistic_data.ipynb index 1ceb959..c2bc20f 100644 --- a/notebooks/notebook_compare_statistic_data.ipynb +++ b/notebooks/notebook_compare_statistic_data.ipynb @@ -5,7 +5,9 @@ "id": "e79f465a", "metadata": {}, "source": [ - "# info" + "# Notebook - Compare Statistic data\n", + "\n", + "Denne notebooken henter data fra to ønskede steder i Norge, og henter historisk statistisk data, for sammenligning og plotting." ] }, { @@ -92,7 +94,7 @@ "Skriv inn navn for til filen du vil lagre med dataen.\n", "\n", "Eks. test\n", - "Da vil filen lagres som data_**test**.json, i mappen \"../data/output_sammenligning_statistikk/data_{filnavn}.json\"\n" + "Da vil filen lagres som data_**test**.json, i mappen `./data/output_sammenligning_statistikk/data_{filnavn}.json`\n" ] }, { diff --git a/notebooks/notebook_one_day_data.ipynb b/notebooks/notebook_one_day_data.ipynb index b88b58c..6178c29 100644 --- a/notebooks/notebook_one_day_data.ipynb +++ b/notebooks/notebook_one_day_data.ipynb @@ -463,10 +463,10 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Lineær regresjons og prediktiv analyse\n", + "### Lineær regresjons og prediktiv analyse\n", "\n", - "lager en lineær regresjon og bruker den til å finne temperaturer vi kan forvente å få samme tid, neste år.\n", - "Bruker scikit-learn biblioteket, sklearn.linear_model, LinearRegression for å få til dette" + "Lager en lineær regresjon og bruker den til å finne temperaturer vi kan forvente å få samme tid, neste år.\n", + "Bruker scikit-learn biblioteket, sklearn.linear_model, LinearRegression for å få til dette." ] }, { @@ -494,7 +494,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### klargjøre data til regresjonsmodellen" + "### Klargjøre data til regresjonsmodellen\n", + "\n", + "Klargjør dataen, og lagrer en regresjosnmodel ved bruk av `LinearRegression()`." ] }, { @@ -516,11 +518,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### trene modellen og predikerte fremtidig data\n", - "\n", - "trene modellen og lager en graf som viser den originale dataen fra årets dag brukeren har skrevet inn og regresjons modellen\n", + "### Trene modellen og predikerte fremtidig data\n", "\n", - "bruker den trente modellen til å lage predikasjoner for temperaturen samme dag, neste år" + "Gir dataen inn til modellen, som predikterer verdien." ] }, { diff --git a/notebooks/notebook_one_week_data.ipynb b/notebooks/notebook_one_week_data.ipynb index e315da3..29c59a8 100644 --- a/notebooks/notebook_one_week_data.ipynb +++ b/notebooks/notebook_one_week_data.ipynb @@ -225,7 +225,7 @@ "plt.figure(figsize=(12, 6))\n", "\n", "# Scatter plot for each temperature reading\n", - "plt.scatter(x_axis, temp, color='tab:red', label='Temperaturmålinger', alpha=0.7)\n", + "plt.scatter(x_axis, temp, color='tab:red', label='Temperature', alpha=0.7)\n", "\n", "# Add a horizontal line for the mean temperature\n", "plt.axhline(y=temp_mean, color='tab:red', linestyle=\"dashed\", label=f'Gj.snitt {temp_mean}°C')\n", @@ -244,7 +244,7 @@ "plt.tight_layout()\n", "\n", "# Add title for the plot\n", - "plt.title(f'Temperatur {city_name}, ({start_date} to {end_date})')\n", + "plt.title(f'Temperature {city_name}, ({start_date} to {end_date})')\n", "\n", "# Add marker at 0 temperature\n", "plt.axhline(y=0, color='black', linewidth=1.5)\n", diff --git a/notebooks/notebook_regression.ipynb b/notebooks/notebook_regression.ipynb new file mode 100644 index 0000000..4bd9c08 --- /dev/null +++ b/notebooks/notebook_regression.ipynb @@ -0,0 +1,303 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "2f91fd0c", + "metadata": {}, + "source": [ + "# Notebook - regression\n", + "\n", + "Denne notebooken henter data fra forhåndslagrede json-filer, før det kjøres ulike metoder for å rydde og renske dataen. Deretter blir dataen kjørt gjennom og trener opp en regresjonsmodell. Tilslutt kan man velge ulike inputs på variablene, for å få prediktert temperatur.\n" + ] + }, + { + "cell_type": "markdown", + "id": "8b1add79", + "metadata": {}, + "source": [ + "### Lese fra testdata fil\n", + "I `data/test` mappen finner vi forhåndsdefinerte dataer som:\n", + "- `merged_data_mars.json` data hentet fra Trondheim, Mars måned 2025. \n", + "- `merged_data_2024_mai_2025.json` data hentet fra Trondheim, fra mai 2024 til mai 2025.\n", + "\n", + "Du kan endre hvilken fil du vil hente data fra ved å kommentere ut filepath." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "961996fc", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import os\n", + "import sys\n", + "\n", + "# Gets the absolute path to the src folder\n", + "sys.path.append(os.path.abspath(\"../src\"))\n", + "\n", + "from my_package.data import extract_city_df\n", + "\n", + "# Data for et helt år\n", + "# filepath = '../data/test/merged_data_2024_mai_2025.json'\n", + "\n", + "# Data for mars måned 2025\n", + "filepath = '../data/test/merged_data_mars.json'\n", + "\n", + "# Reads from file using pandas\n", + "weather_data = pd.read_json(filepath)\n", + "\n", + "# Extract data from weather_data\n", + "df = extract_city_df(weather_data)\n", + "\n", + "# Display the data\n", + "display(df)" + ] + }, + { + "cell_type": "markdown", + "id": "2b142c69", + "metadata": {}, + "source": [ + "### Sjekker og renser data\n", + "\n", + "Denne cellen sjekker at ønskede kolonner er i datasettet. Vi definerer deretter hvilke kolonner vi vil skal fylles med 0, dersom det er manglende verdier. Disse kolonnene er 'rain.1h' og 'snow.1h' da \"NaN\" i disse kolonnene, ofte vil tilsi at det ikke har regnet eller snødd.\n", + "\n", + "Kolonnene som ikke skal fylles med 0, brukes det interpolate på manglende verdier. \"Limit-direction\": brukes for å sørge for at interpolate også fyller første og siste verdi, der det bare er verdi på en side." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a9888d10", + "metadata": {}, + "outputs": [], + "source": [ + "from my_package.util import ensure_column\n", + "from my_package.util import fill_column_0\n", + "\n", + "# The columns we want to check if exsist\n", + "columns_to_ensure = ['rain.1h', 'snow.1h', 'wind.speed', 'wind.gust']\n", + "\n", + "# Runs the function with wanted colummns\n", + "df = ensure_column(df, columns_to_ensure)\n", + "\n", + "# The columns we want to replace 'NaN' with 0\n", + "columns_to_0 = ['rain.1h', 'snow.1h']\n", + "\n", + "# Runs the function with wanted columns\n", + "df = fill_column_0(df, columns_to_0)\n", + "\n", + "# Drops all the columns, if it has 'NaN' value.\n", + "df = df.dropna(axis='columns', how='all')\n", + "\n", + "# Interpolate other missing 'NaN'-values\n", + "df = df.interpolate(method='linear', limit_direction='both')\n", + "\n", + "# Display the data\n", + "display(df)" + ] + }, + { + "cell_type": "markdown", + "id": "0ff7a976", + "metadata": {}, + "source": [ + "### Definere variabler til regresjon\n", + "\n", + "Her definerer vi målvariable, og hvilke variabler som det skal sjekkes sammenheng mellom for å prediktere målvariabelen som i dette tilfellet er temperatur." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cf0d7e0f", + "metadata": {}, + "outputs": [], + "source": [ + "# Variables to see context and affect and wanted variabel\n", + "X = df[['main.humidity', 'wind.speed', 'main.pressure', 'wind.gust', 'clouds.all', 'rain.1h', 'snow.1h']]\n", + "\n", + "# Wanted variabel\n", + "y = df['main.temp']" + ] + }, + { + "cell_type": "markdown", + "id": "fbf681de", + "metadata": {}, + "source": [ + "### Splitte og trene model\n", + "\n", + "Vi splitter dataen inn i train og test, hvor test-size er 0.2, det vil si at 80% av dataen går til trening av modellen, mens 20% går til testingen." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6e999ab3", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "from sklearn.linear_model import LinearRegression\n", + "\n", + "# Split data to train and test, test-size 0.2, random-state 42, normal value\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n", + "\n", + "# Make a linear regression model\n", + "model = LinearRegression()\n", + "\n", + "# Gives the model the train data\n", + "model.fit(X_train, y_train)" + ] + }, + { + "cell_type": "markdown", + "id": "d10f1a7b", + "metadata": {}, + "source": [ + "### Prediksjon\n", + "\n", + "Denne cellen kjører test dataen gjennom den opptrente modellen for å prediktere data.\n", + "\n", + "Viser også ulike variabler som forteller oss hvor god modellen vår er:\n", + "- Coefficients: viser hvor mye de ulike variablene påvirker målvariabelen.\n", + "- Mean squared error(MSE): hvor langt unna er modellen virkeligheten.\n", + "- R2 score: overordnet hvor god modellen er." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2218629a", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.metrics import mean_squared_error, r2_score\n", + "\n", + "# Predicts values based on the trained model with the test, the last 20%\n", + "predictions = model.predict(X_test)\n", + "\n", + "# Values predicted from the model\n", + "print(\"Predicted values:\", predictions)\n", + "\n", + "# Just to get space between\n", + "print()\n", + "\n", + "# The coefficient, the value of each input\n", + "print(\"Coefficients:\", model.coef_)\n", + "\n", + "# The mean squared error, how far the predictions are on average\n", + "print(\"Mean squared error: {:.2f}\".format(mean_squared_error(y_test, predictions)))\n", + "\n", + "# The R2-score or coefficient of determination, how well the the model fits the data\n", + "print(\"R2 score: {:.2f}\".format(r2_score(y_test, predictions)))" + ] + }, + { + "cell_type": "markdown", + "id": "eec0bdd1", + "metadata": {}, + "source": [ + "### Plot av prediktert og faktisk data\n", + "\n", + "Her plottes den predikterte dataen mot den faktiske dataen. I de fleste tilfeller kan vi se at den predikterte dataen følger store deler av den faktiske. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8c726b19", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "# Choose the width and height of the plot\n", + "plt.figure(figsize=(12, 6))\n", + "\n", + "# Plot the actual data from the test\n", + "plt.plot(y_test.values, label='Actual', color='green', alpha=0.6)\n", + "\n", + "# Plot the predicted data\n", + "plt.plot(predictions, label='Predicted', color='red')\n", + "\n", + "# Show legend\n", + "plt.legend(loc='upper right')\n", + "\n", + "# Add title for the plot\n", + "plt.title('Weather Prediction')\n", + "\n", + "# Add label for the x-axis\n", + "plt.xlabel('Sample')\n", + "\n", + "# Add label for the y-axis\n", + "plt.ylabel('Temperature')\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "e0753a0b", + "metadata": {}, + "source": [ + "### Gi input, få prediktert temperatur\n", + "\n", + "Denne funksjonen ber om input for de ulike variablene, før den kjører de gjennom den trente modellen som returnerer prediktert data basert på de ulike verdiene." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7abe08ad", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "# Input for the different variables\n", + "humidity = float(input(\"Enter humidity (%): \"))\n", + "wind_speed = float(input(\"Enter wind speed (km/h): \"))\n", + "pressure = float(input(\"Enter pressure (hPa): \"))\n", + "wind_gust = float(input(\"Enter wind gust (km/h): \"))\n", + "clouds = float(input(\"Enter cloud coverage (%): \"))\n", + "rain = float(input(\"Enter rain (mm): \"))\n", + "snow = float(input(\"Enter snow (mm): \"))\n", + "\n", + "# Stores all the inputed data in an array\n", + "new_data = np.array([[humidity, wind_speed, pressure, wind_gust, clouds, rain, snow]])\n", + "\n", + "# Use the model, with the new data\n", + "predicted_temp = model.predict(new_data)\n", + "\n", + "# Prints the predicted temperature\n", + "print(f\"Predicted Temperature: {predicted_temp[0]:.2f} °C\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/notebook_statistic_data.ipynb b/notebooks/notebook_statistic_data.ipynb index 6e70923..0beeb34 100644 --- a/notebooks/notebook_statistic_data.ipynb +++ b/notebooks/notebook_statistic_data.ipynb @@ -32,7 +32,7 @@ "sys.path.append(os.path.abspath(\"../src\"))\n", "\n", "# Now we can import the fucntion from the module\n", - "from my_package.year_data import fetch_data\n", + "from my_package.data import fetch_stat_data\n", "\n", "# Import function to for input_place, replace æøå\n", "from my_package.util import input_place\n", @@ -40,7 +40,7 @@ "# User input the city, for the weather\n", "city_name = input_place()\n", "\n", - "data, folder = fetch_data(city_name)" + "data, folder = fetch_stat_data(city_name)" ] }, { @@ -52,7 +52,7 @@ "Skriv inn navn for til filen du vil lagre med dataen.\n", "\n", "Eks. test\n", - "Da vil filen lagres som data_**test**.json, i mappen \"../data/output_statistikk/data_{filnavn}.json\"\n" + "Da vil filen lagres som data_**test**.json, i mappen `../data/output_statistikk/data_{filnavn}.json`\n" ] }, { @@ -64,7 +64,7 @@ "# Gets the absolute path to the src folder\n", "sys.path.append(os.path.abspath(\"../src\"))\n", "\n", - "from my_package.write_data import write_data\n", + "from my_package.data import write_data\n", "\n", "filename = input(\"Write filename: \")\n", "\n", @@ -109,7 +109,7 @@ "outputs": [], "source": [ "import pandas as pd\n", - "from my_package.util import extract_city_data_stat\n", + "from my_package.data import extract_city_data_stat\n", "\n", "df = extract_city_data_stat(data)\n", "display(df)" @@ -227,7 +227,7 @@ "\n", "# Add marker at 0 temperature\n", "ax1.axhline(y=0, color='black', linewidth=1.5)\n", - "ax1.axhline(y=temp_mean, color='red', linestyle=\"dashed\")\n", + "ax1.axhline(y=temp_mean, color='red', linestyle=\"dashed\", label=\"Temperature mean (°C)\")\n", "\n", "# Plot precipitation as bars on the secondary y-axis\n", "ax2 = ax1.twinx()\n", @@ -488,7 +488,7 @@ "# Gets the absolute path to the src folder\n", "sys.path.append(os.path.abspath(\"../src\"))\n", "\n", - "from my_package.get_record import get_records\n", + "from my_package.util import get_records\n", "\n", "summary_df, filename, folder = get_records(df, city_name)" ] @@ -510,7 +510,7 @@ "# Gets the absolute path to the src folder\n", "sys.path.append(os.path.abspath(\"../src\"))\n", "\n", - "from my_package.write_data import write_data\n", + "from my_package.data import write_data\n", "# makes the data 'json-compatible'\n", "json_data = summary_df.to_dict(orient=\"records\")\n", "\n", @@ -539,11 +539,144 @@ " data = json.load(file)\n", "\n", "# Normalize the data for better readability\n", - "df = pd.json_normalize(data)\n", + "df_records = pd.json_normalize(data)\n", "\n", "\n", "# Displays the dataframe\n", - "display(df)" + "display(df_records)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Definere variabler til regresjon\n", + "\n", + "Her definerer vi målvariable, og hvilke variabler som det skal sjekkes sammenheng mellom for å prediktere målvariabelen som i dette tilfellet er temperatur." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Variables to see context and affect and wanted variabel\n", + "X = df[['humidity.mean','wind.mean', 'pressure.mean', 'precipitation.mean', 'clouds.mean']]\n", + "\n", + "# Wanted variabel\n", + "y = df['temp.mean_celsius']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Splitte og trene model\n", + "\n", + "Vi splitter dataen inn i train og test, hvor test-size er 0.2, det vil si at 80% av dataen går til trening av modellen, mens 20% går til testingen." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "from sklearn.linear_model import LinearRegression\n", + "\n", + "# Split data to train and test, test-size 0.2, random-state 42, normal value\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n", + "\n", + "# Make a linear regression model\n", + "model = LinearRegression()\n", + "\n", + "# Gives the model the train data\n", + "model.fit(X_train, y_train)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Prediksjon\n", + "\n", + "Denne cellen kjører test dataen gjennom den opptrente modellen for å prediktere data.\n", + "\n", + "Viser også ulike variabler som forteller oss hvor god modellen vår er:\n", + "- Coefficients: viser vhvor mye de ulike variablene påvirker målvariabelen.\n", + "- Mean squared error(MSE): hvor langt unna er modellen virkeligheten.\n", + "- R2 score: overordnet hvor god modellen er." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.metrics import mean_squared_error, r2_score\n", + "\n", + "# Predicts values based on the trained model with the test, the last 20%\n", + "predictions = model.predict(X_test)\n", + "\n", + "# Values predicted from the model\n", + "print(\"Predicted values:\", predictions)\n", + "\n", + "# Just to get space between\n", + "print()\n", + "\n", + "# The coefficient, the value of each input\n", + "print(\"Coefficients:\", model.coef_)\n", + "\n", + "# The mean squared error, how far the predictions are on average\n", + "print(\"Mean squared error: {:.2f}\".format(mean_squared_error(y_test, predictions)))\n", + "\n", + "# The R2-score or coefficient of determination, how well the the model fits the data\n", + "print(\"R2 score: {:.2f}\".format(r2_score(y_test, predictions)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot av prediktert og faktisk data\n", + "\n", + "Her plottes den predikterte dataen mot den faktiske dataen. I de fleste tilfeller kan vi se at den predikterte dataen følger store deler av den faktiske. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "# Choose the width and height of the plot\n", + "plt.figure(figsize=(12, 6))\n", + "\n", + "# Plot the actual data from the test\n", + "plt.plot(y_test.values, label='Actual', color='green', alpha=0.6)\n", + "\n", + "# Plot the predicted data\n", + "plt.plot(predictions, label='Predicted', color='red')\n", + "\n", + "# Show legend\n", + "plt.legend(loc='upper right')\n", + "\n", + "# Add title for the plot\n", + "plt.title('Weather Prediction')\n", + "\n", + "# Add label for the x-axis\n", + "plt.xlabel('Sample')\n", + "\n", + "# Add label for the y-axis\n", + "plt.ylabel('Temperature')\n", + "\n", + "# Show the plot\n", + "plt.show()" ] } ], diff --git a/resources/README.md b/resources/README.md index 957729d..6be3d10 100644 --- a/resources/README.md +++ b/resources/README.md @@ -1,5 +1,7 @@ # Resources - description +## Data ressurser + Kilden til våre API-er er: [Open Weather](https://openweathermap.org/) Her finner vi API-er for: @@ -26,7 +28,7 @@ For å benytte denne API-en må man lage en bruker, og som student for man tilga - **Natural Disasters:** [DATASET_1](https://www.kaggle.com/datasets/brsdincer/all-natural-disasters-19002021-eosdis) -Andre kilder: +## Andre kilder: ### Biblioteker [Pandas](https://pandas.pydata.org/docs/) @@ -39,6 +41,6 @@ Andre kilder: [TDT4114 – Programmering (NTNU)](https://rouhani.folk.ntnu.no/textbooks/tdt4114/intro.html) ### Videomateriale -1. [YouTube: Python Crash Course 1](https://www.youtube.com/watch?v=bDhvCp3_lYw&t=1353s) -2. [YouTube: Python Crash Course 2](https://www.youtube.com/watch?v=b0L47BeklTE) -3. [YouTube: Python Crash Course 3](https://www.youtube.com/watch?v=ukZn2RJb7TU) +1. [YouTube: Alex the analysist (Data cleaning in Pandas)](https://www.youtube.com/watch?v=bDhvCp3_lYw&t=1353s) +2. [YouTube: Python Marathon (Linear Regression Python Sklearn [FROM SCRATCH])](https://www.youtube.com/watch?v=b0L47BeklTE) +3. [YouTube: Ryan & Matt Data Science (Hands-On Linear Regression with Scikit-Learn in Python (Beginner Friendly))](https://www.youtube.com/watch?v=ukZn2RJb7TU)